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Hyperparameter Tuning
Hyperparameter Tuning ¶
hyperparameter -the process of choosing optimal parameter is called hyperparameter tuning This is available in the scikit-learn Python machine learning library.
Parameter¶
A parameter generally, is any characteristic that can help in defining or classifying a particular system Model parameters are the properties of training data that will learn on its own during training by the classifier or other ML model. These are the Factors which contribute in improving the performance of an algorithm.
Hyperparameter ¶
It allows us to choose correct parameter to increase the accuracy score Hyperparameter is a parameter which are set before the algorithm is trained.
In [14]:
import pandas as pd import numpy as np from sklearn.metrics import classification_report, confusion_matrix from sklearn.datasets import load_breast_cancer from sklearn.svm import SVC cancer = load_breast_cancer() # The data set is presented in a dictionary form: print(cancer.keys())
dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])
In [15]:
#we will extract all features into the new dataframe and our target features into separate dataframe. df_feat = pd.DataFrame(cancer['data'], columns = cancer['feature_names']) # cancer column is our target df_target = pd.DataFrame(cancer['target'], columns =['Cancer']) print("Feature Variables: ") print(df_feat.info()) # it will tell about the data types
Feature Variables: <class 'pandas.core.frame.DataFrame'> RangeIndex: 569 entries, 0 to 568 Data columns (total 30 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 mean radius 569 non-null float64 1 mean texture 569 non-null float64 2 mean perimeter 569 non-null float64 3 mean area 569 non-null float64 4 mean smoothness 569 non-null float64 5 mean compactness 569 non-null float64 6 mean concavity 569 non-null float64 7 mean concave points 569 non-null float64 8 mean symmetry 569 non-null float64 9 mean fractal dimension 569 non-null float64 10 radius error 569 non-null float64 11 texture error 569 non-null float64 12 perimeter error 569 non-null float64 13 area error 569 non-null float64 14 smoothness error 569 non-null float64 15 compactness error 569 non-null float64 16 concavity error 569 non-null float64 17 concave points error 569 non-null float64 18 symmetry error 569 non-null float64 19 fractal dimension error 569 non-null float64 20 worst radius 569 non-null float64 21 worst texture 569 non-null float64 22 worst perimeter 569 non-null float64 23 worst area 569 non-null float64 24 worst smoothness 569 non-null float64 25 worst compactness 569 non-null float64 26 worst concavity 569 non-null float64 27 worst concave points 569 non-null float64 28 worst symmetry 569 non-null float64 29 worst fractal dimension 569 non-null float64 dtypes: float64(30) memory usage: 133.5 KB None
In [16]:
print("Dataframe looks like : ") print(df_feat.head())
Dataframe looks like : mean radius mean texture mean perimeter mean area mean smoothness \ 0 17.99 10.38 122.80 1001.0 0.11840 1 20.57 17.77 132.90 1326.0 0.08474 2 19.69 21.25 130.00 1203.0 0.10960 3 11.42 20.38 77.58 386.1 0.14250 4 20.29 14.34 135.10 1297.0 0.10030 mean compactness mean concavity mean concave points mean symmetry \ 0 0.27760 0.3001 0.14710 0.2419 1 0.07864 0.0869 0.07017 0.1812 2 0.15990 0.1974 0.12790 0.2069 3 0.28390 0.2414 0.10520 0.2597 4 0.13280 0.1980 0.10430 0.1809 mean fractal dimension ... worst radius worst texture worst perimeter \ 0 0.07871 ... 25.38 17.33 184.60 1 0.05667 ... 24.99 23.41 158.80 2 0.05999 ... 23.57 25.53 152.50 3 0.09744 ... 14.91 26.50 98.87 4 0.05883 ... 22.54 16.67 152.20 worst area worst smoothness worst compactness worst concavity \ 0 2019.0 0.1622 0.6656 0.7119 1 1956.0 0.1238 0.1866 0.2416 2 1709.0 0.1444 0.4245 0.4504 3 567.7 0.2098 0.8663 0.6869 4 1575.0 0.1374 0.2050 0.4000 worst concave points worst symmetry worst fractal dimension 0 0.2654 0.4601 0.11890 1 0.1860 0.2750 0.08902 2 0.2430 0.3613 0.08758 3 0.2575 0.6638 0.17300 4 0.1625 0.2364 0.07678 [5 rows x 30 columns]
ravel : it is used to change a 2-dimensional array or a multi-dimensional array into a contiguous flattened array. The returned array has the same data type as the source array or input array. ¶
In [17]:
from sklearn.model_selection import train_test_split #split our data into train and test set with 70 : 30 ratio X_train, X_test, y_train, y_test = train_test_split( df_feat, np.ravel(df_target), test_size = 0.30, random_state = 101)
A confusion matrix is a tabular summary of the number of correct and incorrect predictions made by a classifier ¶
In [18]:
# First, we will train our model by calling standard SVC() function #without doing Hyper-parameter Tuning and see its classification and confusion matrix. # train the model on train set model = SVC() model.fit(X_train, y_train) # print prediction results predictions = model.predict(X_test) print(classification_report(y_test, predictions))
precision recall f1-score support
0 0.95 0.85 0.90 66
1 0.91 0.97 0.94 105
accuracy 0.92 171
macro avg 0.93 0.91 0.92 171
weighted avg 0.93 0.92 0.92 171
Use of Grid searching for hyperparameters¶
Grid search is an approach to hyperparameter tuning that will methodically build and evaluate a model for each combination of algorithm parameters specified in a grid.
In [19]:
from sklearn.model_selection import GridSearchCV # defining parameter range param_grid = {'C': [0.1, 1, 10, 100, 1000], 'gamma': [1, 0.1, 0.01, 0.001, 0.0001], 'kernel': ['rbf','linear']} #Refit an estimator using the best found parameters on the whole dataset and its defaulr value is true # verbose: Controls the verbosity: the higher, the more messages. grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 3) # fitting the model for grid search grid.fit(X_train, y_train)
Fitting 5 folds for each of 50 candidates, totalling 250 fits [CV] C=0.1, gamma=1, kernel=rbf ...................................... [CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.637, total= 0.0s [CV] C=0.1, gamma=1, kernel=rbf ...................................... [CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.637, total= 0.0s [CV] C=0.1, gamma=1, kernel=rbf ...................................... [CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.625, total= 0.0s [CV] C=0.1, gamma=1, kernel=rbf ...................................... [CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.633, total= 0.0s [CV] C=0.1, gamma=1, kernel=rbf ...................................... [CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.633, total= 0.0s [CV] C=0.1, gamma=1, kernel=linear ................................... [CV] ....... C=0.1, gamma=1, kernel=linear, score=0.950, total= 0.0s [CV] C=0.1, gamma=1, kernel=linear ................................... [CV] ....... C=0.1, gamma=1, kernel=linear, score=0.925, total= 0.0s [CV] C=0.1, gamma=1, kernel=linear ................................... [CV] ....... C=0.1, gamma=1, kernel=linear, score=0.988, total= 0.0s [CV] C=0.1, gamma=1, kernel=linear ...................................
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.0s remaining: 0.0s
[CV] ....... C=0.1, gamma=1, kernel=linear, score=0.937, total= 0.0s [CV] C=0.1, gamma=1, kernel=linear ................................... [CV] ....... C=0.1, gamma=1, kernel=linear, score=0.962, total= 0.1s [CV] C=0.1, gamma=0.1, kernel=rbf .................................... [CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.637, total= 0.0s [CV] C=0.1, gamma=0.1, kernel=rbf .................................... [CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.637, total= 0.0s [CV] C=0.1, gamma=0.1, kernel=rbf .................................... [CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.625, total= 0.0s [CV] C=0.1, gamma=0.1, kernel=rbf .................................... [CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.633, total= 0.0s [CV] C=0.1, gamma=0.1, kernel=rbf .................................... [CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.633, total= 0.0s [CV] C=0.1, gamma=0.1, kernel=linear ................................. [CV] ..... C=0.1, gamma=0.1, kernel=linear, score=0.950, total= 0.0s [CV] C=0.1, gamma=0.1, kernel=linear ................................. [CV] ..... C=0.1, gamma=0.1, kernel=linear, score=0.925, total= 0.0s [CV] C=0.1, gamma=0.1, kernel=linear ................................. [CV] ..... C=0.1, gamma=0.1, kernel=linear, score=0.988, total= 0.0s [CV] C=0.1, gamma=0.1, kernel=linear ................................. [CV] ..... C=0.1, gamma=0.1, kernel=linear, score=0.937, total= 0.0s [CV] C=0.1, gamma=0.1, kernel=linear ................................. [CV] ..... C=0.1, gamma=0.1, kernel=linear, score=0.962, total= 0.1s [CV] C=0.1, gamma=0.01, kernel=rbf ................................... [CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.637, total= 0.0s [CV] C=0.1, gamma=0.01, kernel=rbf ................................... [CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.637, total= 0.0s [CV] C=0.1, gamma=0.01, kernel=rbf ................................... [CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.625, total= 0.0s [CV] C=0.1, gamma=0.01, kernel=rbf ................................... [CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.633, total= 0.0s [CV] C=0.1, gamma=0.01, kernel=rbf ................................... [CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.633, total= 0.0s [CV] C=0.1, gamma=0.01, kernel=linear ................................ [CV] .... C=0.1, gamma=0.01, kernel=linear, score=0.950, total= 0.0s [CV] C=0.1, gamma=0.01, kernel=linear ................................ [CV] .... C=0.1, gamma=0.01, kernel=linear, score=0.925, total= 0.0s [CV] C=0.1, gamma=0.01, kernel=linear ................................ [CV] .... C=0.1, gamma=0.01, kernel=linear, score=0.988, total= 0.0s [CV] C=0.1, gamma=0.01, kernel=linear ................................ [CV] .... C=0.1, gamma=0.01, kernel=linear, score=0.937, total= 0.0s [CV] C=0.1, gamma=0.01, kernel=linear ................................ [CV] .... C=0.1, gamma=0.01, kernel=linear, score=0.962, total= 0.1s [CV] C=0.1, gamma=0.001, kernel=rbf .................................. [CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.637, total= 0.0s [CV] C=0.1, gamma=0.001, kernel=rbf .................................. [CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.637, total= 0.0s [CV] C=0.1, gamma=0.001, kernel=rbf .................................. [CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.625, total= 0.0s [CV] C=0.1, gamma=0.001, kernel=rbf .................................. [CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.633, total= 0.0s [CV] C=0.1, gamma=0.001, kernel=rbf .................................. [CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.633, total= 0.0s [CV] C=0.1, gamma=0.001, kernel=linear ............................... [CV] ... C=0.1, gamma=0.001, kernel=linear, score=0.950, total= 0.0s [CV] C=0.1, gamma=0.001, kernel=linear ............................... [CV] ... C=0.1, gamma=0.001, kernel=linear, score=0.925, total= 0.0s [CV] C=0.1, gamma=0.001, kernel=linear ............................... [CV] ... C=0.1, gamma=0.001, kernel=linear, score=0.988, total= 0.0s [CV] C=0.1, gamma=0.001, kernel=linear ............................... [CV] ... C=0.1, gamma=0.001, kernel=linear, score=0.937, total= 0.0s [CV] C=0.1, gamma=0.001, kernel=linear ............................... [CV] ... C=0.1, gamma=0.001, kernel=linear, score=0.962, total= 0.1s [CV] C=0.1, gamma=0.0001, kernel=rbf ................................. [CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.887, total= 0.0s [CV] C=0.1, gamma=0.0001, kernel=rbf ................................. [CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.938, total= 0.0s [CV] C=0.1, gamma=0.0001, kernel=rbf ................................. [CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.963, total= 0.0s [CV] C=0.1, gamma=0.0001, kernel=rbf ................................. [CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.962, total= 0.0s [CV] C=0.1, gamma=0.0001, kernel=rbf ................................. [CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.886, total= 0.0s [CV] C=0.1, gamma=0.0001, kernel=linear .............................. [CV] .. C=0.1, gamma=0.0001, kernel=linear, score=0.950, total= 0.0s [CV] C=0.1, gamma=0.0001, kernel=linear .............................. [CV] .. C=0.1, gamma=0.0001, kernel=linear, score=0.925, total= 0.0s [CV] C=0.1, gamma=0.0001, kernel=linear .............................. [CV] .. C=0.1, gamma=0.0001, kernel=linear, score=0.988, total= 0.0s [CV] C=0.1, gamma=0.0001, kernel=linear .............................. [CV] .. C=0.1, gamma=0.0001, kernel=linear, score=0.937, total= 0.0s [CV] C=0.1, gamma=0.0001, kernel=linear .............................. [CV] .. C=0.1, gamma=0.0001, kernel=linear, score=0.962, total= 0.1s [CV] C=1, gamma=1, kernel=rbf ........................................ [CV] ............ C=1, gamma=1, kernel=rbf, score=0.637, total= 0.0s [CV] C=1, gamma=1, kernel=rbf ........................................ [CV] ............ C=1, gamma=1, kernel=rbf, score=0.637, total= 0.0s [CV] C=1, gamma=1, kernel=rbf ........................................ [CV] ............ C=1, gamma=1, kernel=rbf, score=0.625, total= 0.0s [CV] C=1, gamma=1, kernel=rbf ........................................ [CV] ............ C=1, gamma=1, kernel=rbf, score=0.633, total= 0.0s [CV] C=1, gamma=1, kernel=rbf ........................................ [CV] ............ C=1, gamma=1, kernel=rbf, score=0.633, total= 0.0s [CV] C=1, gamma=1, kernel=linear ..................................... [CV] ......... C=1, gamma=1, kernel=linear, score=0.950, total= 0.6s [CV] C=1, gamma=1, kernel=linear ..................................... [CV] ......... C=1, gamma=1, kernel=linear, score=0.938, total= 0.4s [CV] C=1, gamma=1, kernel=linear ..................................... [CV] ......... C=1, gamma=1, kernel=linear, score=1.000, total= 0.6s [CV] C=1, gamma=1, kernel=linear ..................................... [CV] ......... C=1, gamma=1, kernel=linear, score=0.937, total= 0.6s [CV] C=1, gamma=1, kernel=linear ..................................... [CV] ......... C=1, gamma=1, kernel=linear, score=0.987, total= 0.3s [CV] C=1, gamma=0.1, kernel=rbf ...................................... [CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.637, total= 0.0s [CV] C=1, gamma=0.1, kernel=rbf ...................................... [CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.637, total= 0.0s [CV] C=1, gamma=0.1, kernel=rbf ...................................... [CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.625, total= 0.0s [CV] C=1, gamma=0.1, kernel=rbf ...................................... [CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.633, total= 0.0s [CV] C=1, gamma=0.1, kernel=rbf ...................................... [CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.633, total= 0.0s [CV] C=1, gamma=0.1, kernel=linear ................................... [CV] ....... C=1, gamma=0.1, kernel=linear, score=0.950, total= 0.6s [CV] C=1, gamma=0.1, kernel=linear ................................... [CV] ....... C=1, gamma=0.1, kernel=linear, score=0.938, total= 0.4s [CV] C=1, gamma=0.1, kernel=linear ................................... [CV] ....... C=1, gamma=0.1, kernel=linear, score=1.000, total= 0.6s [CV] C=1, gamma=0.1, kernel=linear ................................... [CV] ....... C=1, gamma=0.1, kernel=linear, score=0.937, total= 0.6s [CV] C=1, gamma=0.1, kernel=linear ................................... [CV] ....... C=1, gamma=0.1, kernel=linear, score=0.987, total= 0.3s [CV] C=1, gamma=0.01, kernel=rbf ..................................... [CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.637, total= 0.0s [CV] C=1, gamma=0.01, kernel=rbf ..................................... [CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.637, total= 0.0s [CV] C=1, gamma=0.01, kernel=rbf ..................................... [CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.625, total= 0.0s [CV] C=1, gamma=0.01, kernel=rbf ..................................... [CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.633, total= 0.0s [CV] C=1, gamma=0.01, kernel=rbf ..................................... [CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.633, total= 0.0s [CV] C=1, gamma=0.01, kernel=linear .................................. [CV] ...... C=1, gamma=0.01, kernel=linear, score=0.950, total= 0.6s [CV] C=1, gamma=0.01, kernel=linear .................................. [CV] ...... C=1, gamma=0.01, kernel=linear, score=0.938, total= 0.4s [CV] C=1, gamma=0.01, kernel=linear .................................. [CV] ...... C=1, gamma=0.01, kernel=linear, score=1.000, total= 0.6s [CV] C=1, gamma=0.01, kernel=linear .................................. [CV] ...... C=1, gamma=0.01, kernel=linear, score=0.937, total= 0.6s [CV] C=1, gamma=0.01, kernel=linear .................................. [CV] ...... C=1, gamma=0.01, kernel=linear, score=0.987, total= 0.3s [CV] C=1, gamma=0.001, kernel=rbf .................................... [CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.900, total= 0.0s [CV] C=1, gamma=0.001, kernel=rbf .................................... [CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.912, total= 0.0s [CV] C=1, gamma=0.001, kernel=rbf .................................... [CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.925, total= 0.0s [CV] C=1, gamma=0.001, kernel=rbf .................................... [CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.962, total= 0.0s [CV] C=1, gamma=0.001, kernel=rbf .................................... [CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.937, total= 0.0s [CV] C=1, gamma=0.001, kernel=linear ................................. [CV] ..... C=1, gamma=0.001, kernel=linear, score=0.950, total= 0.6s [CV] C=1, gamma=0.001, kernel=linear ................................. [CV] ..... C=1, gamma=0.001, kernel=linear, score=0.938, total= 0.4s [CV] C=1, gamma=0.001, kernel=linear ................................. [CV] ..... C=1, gamma=0.001, kernel=linear, score=1.000, total= 0.6s [CV] C=1, gamma=0.001, kernel=linear ................................. [CV] ..... C=1, gamma=0.001, kernel=linear, score=0.937, total= 0.6s [CV] C=1, gamma=0.001, kernel=linear ................................. [CV] ..... C=1, gamma=0.001, kernel=linear, score=0.987, total= 0.3s [CV] C=1, gamma=0.0001, kernel=rbf ................................... [CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.912, total= 0.0s [CV] C=1, gamma=0.0001, kernel=rbf ................................... [CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.950, total= 0.0s [CV] C=1, gamma=0.0001, kernel=rbf ................................... [CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.975, total= 0.0s [CV] C=1, gamma=0.0001, kernel=rbf ................................... [CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.962, total= 0.0s [CV] C=1, gamma=0.0001, kernel=rbf ................................... [CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.937, total= 0.0s [CV] C=1, gamma=0.0001, kernel=linear ................................ [CV] .... C=1, gamma=0.0001, kernel=linear, score=0.950, total= 0.6s [CV] C=1, gamma=0.0001, kernel=linear ................................ [CV] .... C=1, gamma=0.0001, kernel=linear, score=0.938, total= 0.4s [CV] C=1, gamma=0.0001, kernel=linear ................................ [CV] .... C=1, gamma=0.0001, kernel=linear, score=1.000, total= 0.6s [CV] C=1, gamma=0.0001, kernel=linear ................................ [CV] .... C=1, gamma=0.0001, kernel=linear, score=0.937, total= 0.6s [CV] C=1, gamma=0.0001, kernel=linear ................................ [CV] .... C=1, gamma=0.0001, kernel=linear, score=0.987, total= 0.3s [CV] C=10, gamma=1, kernel=rbf ....................................... [CV] ........... C=10, gamma=1, kernel=rbf, score=0.637, total= 0.0s [CV] C=10, gamma=1, kernel=rbf ....................................... [CV] ........... C=10, gamma=1, kernel=rbf, score=0.637, total= 0.0s [CV] C=10, gamma=1, kernel=rbf ....................................... [CV] ........... C=10, gamma=1, kernel=rbf, score=0.625, total= 0.0s [CV] C=10, gamma=1, kernel=rbf ....................................... [CV] ........... C=10, gamma=1, kernel=rbf, score=0.633, total= 0.0s [CV] C=10, gamma=1, kernel=rbf ....................................... [CV] ........... C=10, gamma=1, kernel=rbf, score=0.633, total= 0.0s [CV] C=10, gamma=1, kernel=linear .................................... [CV] ........ C=10, gamma=1, kernel=linear, score=0.938, total= 3.4s [CV] C=10, gamma=1, kernel=linear .................................... [CV] ........ C=10, gamma=1, kernel=linear, score=0.938, total= 1.1s [CV] C=10, gamma=1, kernel=linear .................................... [CV] ........ C=10, gamma=1, kernel=linear, score=1.000, total= 1.7s [CV] C=10, gamma=1, kernel=linear .................................... [CV] ........ C=10, gamma=1, kernel=linear, score=0.949, total= 2.8s [CV] C=10, gamma=1, kernel=linear .................................... [CV] ........ C=10, gamma=1, kernel=linear, score=0.987, total= 3.3s [CV] C=10, gamma=0.1, kernel=rbf ..................................... [CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.637, total= 0.0s [CV] C=10, gamma=0.1, kernel=rbf ..................................... [CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.637, total= 0.0s [CV] C=10, gamma=0.1, kernel=rbf ..................................... [CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.625, total= 0.0s [CV] C=10, gamma=0.1, kernel=rbf ..................................... [CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.633, total= 0.0s [CV] C=10, gamma=0.1, kernel=rbf ..................................... [CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.633, total= 0.0s [CV] C=10, gamma=0.1, kernel=linear .................................. [CV] ...... C=10, gamma=0.1, kernel=linear, score=0.938, total= 3.4s [CV] C=10, gamma=0.1, kernel=linear .................................. [CV] ...... C=10, gamma=0.1, kernel=linear, score=0.938, total= 1.1s [CV] C=10, gamma=0.1, kernel=linear .................................. [CV] ...... C=10, gamma=0.1, kernel=linear, score=1.000, total= 1.7s [CV] C=10, gamma=0.1, kernel=linear .................................. [CV] ...... C=10, gamma=0.1, kernel=linear, score=0.949, total= 2.9s [CV] C=10, gamma=0.1, kernel=linear .................................. [CV] ...... C=10, gamma=0.1, kernel=linear, score=0.987, total= 3.3s [CV] C=10, gamma=0.01, kernel=rbf .................................... [CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.637, total= 0.0s [CV] C=10, gamma=0.01, kernel=rbf .................................... [CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.637, total= 0.0s [CV] C=10, gamma=0.01, kernel=rbf .................................... [CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.613, total= 0.0s [CV] C=10, gamma=0.01, kernel=rbf .................................... [CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.633, total= 0.0s [CV] C=10, gamma=0.01, kernel=rbf .................................... [CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.633, total= 0.0s [CV] C=10, gamma=0.01, kernel=linear ................................. [CV] ..... C=10, gamma=0.01, kernel=linear, score=0.938, total= 3.4s [CV] C=10, gamma=0.01, kernel=linear ................................. [CV] ..... C=10, gamma=0.01, kernel=linear, score=0.938, total= 1.1s [CV] C=10, gamma=0.01, kernel=linear ................................. [CV] ..... C=10, gamma=0.01, kernel=linear, score=1.000, total= 1.7s [CV] C=10, gamma=0.01, kernel=linear ................................. [CV] ..... C=10, gamma=0.01, kernel=linear, score=0.949, total= 2.8s [CV] C=10, gamma=0.01, kernel=linear ................................. [CV] ..... C=10, gamma=0.01, kernel=linear, score=0.987, total= 3.3s [CV] C=10, gamma=0.001, kernel=rbf ................................... [CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.887, total= 0.0s [CV] C=10, gamma=0.001, kernel=rbf ................................... [CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.912, total= 0.0s [CV] C=10, gamma=0.001, kernel=rbf ................................... [CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.900, total= 0.0s [CV] C=10, gamma=0.001, kernel=rbf ................................... [CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.937, total= 0.0s [CV] C=10, gamma=0.001, kernel=rbf ................................... [CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.924, total= 0.0s [CV] C=10, gamma=0.001, kernel=linear ................................ [CV] .... C=10, gamma=0.001, kernel=linear, score=0.938, total= 3.4s [CV] C=10, gamma=0.001, kernel=linear ................................ [CV] .... C=10, gamma=0.001, kernel=linear, score=0.938, total= 1.1s [CV] C=10, gamma=0.001, kernel=linear ................................ [CV] .... C=10, gamma=0.001, kernel=linear, score=1.000, total= 1.8s [CV] C=10, gamma=0.001, kernel=linear ................................ [CV] .... C=10, gamma=0.001, kernel=linear, score=0.949, total= 2.9s [CV] C=10, gamma=0.001, kernel=linear ................................ [CV] .... C=10, gamma=0.001, kernel=linear, score=0.987, total= 3.3s [CV] C=10, gamma=0.0001, kernel=rbf .................................. [CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.950, total= 0.0s [CV] C=10, gamma=0.0001, kernel=rbf .................................. [CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.912, total= 0.0s [CV] C=10, gamma=0.0001, kernel=rbf .................................. [CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.975, total= 0.0s [CV] C=10, gamma=0.0001, kernel=rbf .................................. [CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.949, total= 0.0s [CV] C=10, gamma=0.0001, kernel=rbf .................................. [CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.949, total= 0.0s [CV] C=10, gamma=0.0001, kernel=linear ............................... [CV] ... C=10, gamma=0.0001, kernel=linear, score=0.938, total= 3.4s [CV] C=10, gamma=0.0001, kernel=linear ............................... [CV] ... C=10, gamma=0.0001, kernel=linear, score=0.938, total= 1.1s [CV] C=10, gamma=0.0001, kernel=linear ............................... [CV] ... C=10, gamma=0.0001, kernel=linear, score=1.000, total= 1.7s [CV] C=10, gamma=0.0001, kernel=linear ............................... [CV] ... C=10, gamma=0.0001, kernel=linear, score=0.949, total= 2.8s [CV] C=10, gamma=0.0001, kernel=linear ............................... [CV] ... C=10, gamma=0.0001, kernel=linear, score=0.987, total= 3.3s [CV] C=100, gamma=1, kernel=rbf ...................................... [CV] .......... C=100, gamma=1, kernel=rbf, score=0.637, total= 0.0s [CV] C=100, gamma=1, kernel=rbf ...................................... [CV] .......... C=100, gamma=1, kernel=rbf, score=0.637, total= 0.0s [CV] C=100, gamma=1, kernel=rbf ...................................... [CV] .......... C=100, gamma=1, kernel=rbf, score=0.625, total= 0.0s [CV] C=100, gamma=1, kernel=rbf ...................................... [CV] .......... C=100, gamma=1, kernel=rbf, score=0.633, total= 0.0s [CV] C=100, gamma=1, kernel=rbf ...................................... [CV] .......... C=100, gamma=1, kernel=rbf, score=0.633, total= 0.0s [CV] C=100, gamma=1, kernel=linear ................................... [CV] ....... C=100, gamma=1, kernel=linear, score=0.950, total= 2.7s [CV] C=100, gamma=1, kernel=linear ................................... [CV] ....... C=100, gamma=1, kernel=linear, score=0.950, total= 1.1s [CV] C=100, gamma=1, kernel=linear ................................... [CV] ....... C=100, gamma=1, kernel=linear, score=1.000, total= 5.1s [CV] C=100, gamma=1, kernel=linear ................................... [CV] ....... C=100, gamma=1, kernel=linear, score=0.949, total= 9.8s [CV] C=100, gamma=1, kernel=linear ................................... [CV] ....... C=100, gamma=1, kernel=linear, score=0.987, total= 5.3s [CV] C=100, gamma=0.1, kernel=rbf .................................... [CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.637, total= 0.0s [CV] C=100, gamma=0.1, kernel=rbf .................................... [CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.637, total= 0.0s [CV] C=100, gamma=0.1, kernel=rbf .................................... [CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.625, total= 0.0s [CV] C=100, gamma=0.1, kernel=rbf .................................... [CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.633, total= 0.0s [CV] C=100, gamma=0.1, kernel=rbf .................................... [CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.633, total= 0.0s [CV] C=100, gamma=0.1, kernel=linear ................................. [CV] ..... C=100, gamma=0.1, kernel=linear, score=0.950, total= 2.7s [CV] C=100, gamma=0.1, kernel=linear ................................. [CV] ..... C=100, gamma=0.1, kernel=linear, score=0.950, total= 1.1s [CV] C=100, gamma=0.1, kernel=linear ................................. [CV] ..... C=100, gamma=0.1, kernel=linear, score=1.000, total= 5.0s [CV] C=100, gamma=0.1, kernel=linear ................................. [CV] ..... C=100, gamma=0.1, kernel=linear, score=0.949, total= 9.6s [CV] C=100, gamma=0.1, kernel=linear ................................. [CV] ..... C=100, gamma=0.1, kernel=linear, score=0.987, total= 5.3s [CV] C=100, gamma=0.01, kernel=rbf ................................... [CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.637, total= 0.0s [CV] C=100, gamma=0.01, kernel=rbf ................................... [CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.637, total= 0.0s [CV] C=100, gamma=0.01, kernel=rbf ................................... [CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.613, total= 0.0s [CV] C=100, gamma=0.01, kernel=rbf ................................... [CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.633, total= 0.0s [CV] C=100, gamma=0.01, kernel=rbf ................................... [CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.633, total= 0.0s [CV] C=100, gamma=0.01, kernel=linear ................................ [CV] .... C=100, gamma=0.01, kernel=linear, score=0.950, total= 2.8s [CV] C=100, gamma=0.01, kernel=linear ................................ [CV] .... C=100, gamma=0.01, kernel=linear, score=0.950, total= 1.1s [CV] C=100, gamma=0.01, kernel=linear ................................ [CV] .... C=100, gamma=0.01, kernel=linear, score=1.000, total= 5.1s [CV] C=100, gamma=0.01, kernel=linear ................................ [CV] .... C=100, gamma=0.01, kernel=linear, score=0.949, total= 9.9s [CV] C=100, gamma=0.01, kernel=linear ................................ [CV] .... C=100, gamma=0.01, kernel=linear, score=0.987, total= 5.4s [CV] C=100, gamma=0.001, kernel=rbf .................................. [CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.887, total= 0.0s [CV] C=100, gamma=0.001, kernel=rbf .................................. [CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.912, total= 0.0s [CV] C=100, gamma=0.001, kernel=rbf .................................. [CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.900, total= 0.0s [CV] C=100, gamma=0.001, kernel=rbf .................................. [CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.937, total= 0.0s [CV] C=100, gamma=0.001, kernel=rbf .................................. [CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.924, total= 0.0s [CV] C=100, gamma=0.001, kernel=linear ............................... [CV] ... C=100, gamma=0.001, kernel=linear, score=0.950, total= 2.8s [CV] C=100, gamma=0.001, kernel=linear ............................... [CV] ... C=100, gamma=0.001, kernel=linear, score=0.950, total= 1.1s [CV] C=100, gamma=0.001, kernel=linear ............................... [CV] ... C=100, gamma=0.001, kernel=linear, score=1.000, total= 5.0s [CV] C=100, gamma=0.001, kernel=linear ............................... [CV] ... C=100, gamma=0.001, kernel=linear, score=0.949, total= 9.8s [CV] C=100, gamma=0.001, kernel=linear ............................... [CV] ... C=100, gamma=0.001, kernel=linear, score=0.987, total= 5.5s [CV] C=100, gamma=0.0001, kernel=rbf ................................. [CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.925, total= 0.0s [CV] C=100, gamma=0.0001, kernel=rbf ................................. [CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.912, total= 0.0s [CV] C=100, gamma=0.0001, kernel=rbf ................................. [CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.975, total= 0.0s [CV] C=100, gamma=0.0001, kernel=rbf ................................. [CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.937, total= 0.0s [CV] C=100, gamma=0.0001, kernel=rbf ................................. [CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.949, total= 0.0s [CV] C=100, gamma=0.0001, kernel=linear .............................. [CV] .. C=100, gamma=0.0001, kernel=linear, score=0.950, total= 2.8s [CV] C=100, gamma=0.0001, kernel=linear .............................. [CV] .. C=100, gamma=0.0001, kernel=linear, score=0.950, total= 1.1s [CV] C=100, gamma=0.0001, kernel=linear .............................. [CV] .. C=100, gamma=0.0001, kernel=linear, score=1.000, total= 5.3s [CV] C=100, gamma=0.0001, kernel=linear .............................. [CV] .. C=100, gamma=0.0001, kernel=linear, score=0.949, total= 10.0s [CV] C=100, gamma=0.0001, kernel=linear .............................. [CV] .. C=100, gamma=0.0001, kernel=linear, score=0.987, total= 5.5s [CV] C=1000, gamma=1, kernel=rbf ..................................... [CV] ......... C=1000, gamma=1, kernel=rbf, score=0.637, total= 0.0s [CV] C=1000, gamma=1, kernel=rbf ..................................... [CV] ......... C=1000, gamma=1, kernel=rbf, score=0.637, total= 0.0s [CV] C=1000, gamma=1, kernel=rbf ..................................... [CV] ......... C=1000, gamma=1, kernel=rbf, score=0.625, total= 0.0s [CV] C=1000, gamma=1, kernel=rbf ..................................... [CV] ......... C=1000, gamma=1, kernel=rbf, score=0.633, total= 0.0s [CV] C=1000, gamma=1, kernel=rbf ..................................... [CV] ......... C=1000, gamma=1, kernel=rbf, score=0.633, total= 0.0s [CV] C=1000, gamma=1, kernel=linear .................................. [CV] ...... C=1000, gamma=1, kernel=linear, score=0.950, total= 7.8s [CV] C=1000, gamma=1, kernel=linear .................................. [CV] ...... C=1000, gamma=1, kernel=linear, score=0.950, total= 1.9s [CV] C=1000, gamma=1, kernel=linear .................................. [CV] ...... C=1000, gamma=1, kernel=linear, score=1.000, total= 2.5s [CV] C=1000, gamma=1, kernel=linear .................................. [CV] ...... C=1000, gamma=1, kernel=linear, score=0.949, total= 7.0s [CV] C=1000, gamma=1, kernel=linear .................................. [CV] ...... C=1000, gamma=1, kernel=linear, score=0.987, total= 7.9s [CV] C=1000, gamma=0.1, kernel=rbf ................................... [CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.637, total= 0.0s [CV] C=1000, gamma=0.1, kernel=rbf ................................... [CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.637, total= 0.0s [CV] C=1000, gamma=0.1, kernel=rbf ................................... [CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.625, total= 0.0s [CV] C=1000, gamma=0.1, kernel=rbf ................................... [CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.633, total= 0.0s [CV] C=1000, gamma=0.1, kernel=rbf ................................... [CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.633, total= 0.0s [CV] C=1000, gamma=0.1, kernel=linear ................................ [CV] .... C=1000, gamma=0.1, kernel=linear, score=0.950, total= 7.8s [CV] C=1000, gamma=0.1, kernel=linear ................................ [CV] .... C=1000, gamma=0.1, kernel=linear, score=0.950, total= 2.0s [CV] C=1000, gamma=0.1, kernel=linear ................................ [CV] .... C=1000, gamma=0.1, kernel=linear, score=1.000, total= 2.5s [CV] C=1000, gamma=0.1, kernel=linear ................................ [CV] .... C=1000, gamma=0.1, kernel=linear, score=0.949, total= 7.2s [CV] C=1000, gamma=0.1, kernel=linear ................................ [CV] .... C=1000, gamma=0.1, kernel=linear, score=0.987, total= 7.9s [CV] C=1000, gamma=0.01, kernel=rbf .................................. [CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.637, total= 0.0s [CV] C=1000, gamma=0.01, kernel=rbf .................................. [CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.637, total= 0.0s [CV] C=1000, gamma=0.01, kernel=rbf .................................. [CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.613, total= 0.0s [CV] C=1000, gamma=0.01, kernel=rbf .................................. [CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.633, total= 0.0s [CV] C=1000, gamma=0.01, kernel=rbf .................................. [CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.633, total= 0.0s [CV] C=1000, gamma=0.01, kernel=linear ............................... [CV] ... C=1000, gamma=0.01, kernel=linear, score=0.950, total= 7.7s [CV] C=1000, gamma=0.01, kernel=linear ............................... [CV] ... C=1000, gamma=0.01, kernel=linear, score=0.950, total= 2.0s [CV] C=1000, gamma=0.01, kernel=linear ............................... [CV] ... C=1000, gamma=0.01, kernel=linear, score=1.000, total= 2.5s [CV] C=1000, gamma=0.01, kernel=linear ............................... [CV] ... C=1000, gamma=0.01, kernel=linear, score=0.949, total= 7.3s [CV] C=1000, gamma=0.01, kernel=linear ............................... [CV] ... C=1000, gamma=0.01, kernel=linear, score=0.987, total= 7.8s [CV] C=1000, gamma=0.001, kernel=rbf ................................. [CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.887, total= 0.0s [CV] C=1000, gamma=0.001, kernel=rbf ................................. [CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.912, total= 0.0s [CV] C=1000, gamma=0.001, kernel=rbf ................................. [CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.900, total= 0.0s [CV] C=1000, gamma=0.001, kernel=rbf ................................. [CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.937, total= 0.0s [CV] C=1000, gamma=0.001, kernel=rbf ................................. [CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.924, total= 0.0s [CV] C=1000, gamma=0.001, kernel=linear .............................. [CV] .. C=1000, gamma=0.001, kernel=linear, score=0.950, total= 7.6s [CV] C=1000, gamma=0.001, kernel=linear .............................. [CV] .. C=1000, gamma=0.001, kernel=linear, score=0.950, total= 2.0s [CV] C=1000, gamma=0.001, kernel=linear .............................. [CV] .. C=1000, gamma=0.001, kernel=linear, score=1.000, total= 2.5s [CV] C=1000, gamma=0.001, kernel=linear .............................. [CV] .. C=1000, gamma=0.001, kernel=linear, score=0.949, total= 7.2s [CV] C=1000, gamma=0.001, kernel=linear .............................. [CV] .. C=1000, gamma=0.001, kernel=linear, score=0.987, total= 7.7s [CV] C=1000, gamma=0.0001, kernel=rbf ................................ [CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.938, total= 0.0s [CV] C=1000, gamma=0.0001, kernel=rbf ................................ [CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.912, total= 0.0s [CV] C=1000, gamma=0.0001, kernel=rbf ................................ [CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.963, total= 0.0s [CV] C=1000, gamma=0.0001, kernel=rbf ................................ [CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.924, total= 0.0s [CV] C=1000, gamma=0.0001, kernel=rbf ................................ [CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.962, total= 0.0s [CV] C=1000, gamma=0.0001, kernel=linear ............................. [CV] . C=1000, gamma=0.0001, kernel=linear, score=0.950, total= 7.6s [CV] C=1000, gamma=0.0001, kernel=linear ............................. [CV] . C=1000, gamma=0.0001, kernel=linear, score=0.950, total= 2.1s [CV] C=1000, gamma=0.0001, kernel=linear ............................. [CV] . C=1000, gamma=0.0001, kernel=linear, score=1.000, total= 2.5s [CV] C=1000, gamma=0.0001, kernel=linear ............................. [CV] . C=1000, gamma=0.0001, kernel=linear, score=0.949, total= 7.3s [CV] C=1000, gamma=0.0001, kernel=linear ............................. [CV] . C=1000, gamma=0.0001, kernel=linear, score=0.987, total= 7.8s
[Parallel(n_jobs=1)]: Done 250 out of 250 | elapsed: 5.6min finished
Out[19]:
GridSearchCV(estimator=SVC(),
param_grid={'C': [0.1, 1, 10, 100, 1000],
'gamma': [1, 0.1, 0.01, 0.001, 0.0001],
'kernel': ['rbf', 'linear']},
verbose=3)
In [20]:
# print best parameter after tuning print(grid.best_params_) # print how our model looks after hyper-parameter tuning print(grid.best_estimator_)
{'C': 100, 'gamma': 1, 'kernel': 'linear'}
SVC(C=100, gamma=1, kernel='linear')
Overview on random search¶
Random search is a technique where random combinations of the hyperparameters are used to find the best solution for the built model.
In [21]:
from sklearn.model_selection import RandomizedSearchCV
In [22]:
grid_predictions = grid.predict(X_test) # print classification report print(classification_report(y_test, grid_predictions))
precision recall f1-score support
0 0.97 0.91 0.94 66
1 0.94 0.98 0.96 105
accuracy 0.95 171
macro avg 0.96 0.95 0.95 171
weighted avg 0.95 0.95 0.95 171
In [23]:
rdm = RandomizedSearchCV(SVC(), param_distributions=param_grid, refit = True, verbose = 3) # fitting the model for random search rdm.fit(X_train, y_train)
Fitting 5 folds for each of 10 candidates, totalling 50 fits [CV] kernel=rbf, gamma=0.1, C=1000 ................................... [CV] ....... kernel=rbf, gamma=0.1, C=1000, score=0.637, total= 0.0s [CV] kernel=rbf, gamma=0.1, C=1000 ................................... [CV] ....... kernel=rbf, gamma=0.1, C=1000, score=0.637, total= 0.0s [CV] kernel=rbf, gamma=0.1, C=1000 ................................... [CV] ....... kernel=rbf, gamma=0.1, C=1000, score=0.625, total= 0.0s [CV] kernel=rbf, gamma=0.1, C=1000 ................................... [CV] ....... kernel=rbf, gamma=0.1, C=1000, score=0.633, total= 0.0s [CV] kernel=rbf, gamma=0.1, C=1000 ................................... [CV] ....... kernel=rbf, gamma=0.1, C=1000, score=0.633, total= 0.0s [CV] kernel=linear, gamma=1, C=0.1 ................................... [CV] ....... kernel=linear, gamma=1, C=0.1, score=0.950, total= 0.0s [CV] kernel=linear, gamma=1, C=0.1 ................................... [CV] ....... kernel=linear, gamma=1, C=0.1, score=0.925, total= 0.0s [CV] kernel=linear, gamma=1, C=0.1 ...................................
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.1s remaining: 0.0s
[CV] ....... kernel=linear, gamma=1, C=0.1, score=0.988, total= 0.0s [CV] kernel=linear, gamma=1, C=0.1 ................................... [CV] ....... kernel=linear, gamma=1, C=0.1, score=0.937, total= 0.0s [CV] kernel=linear, gamma=1, C=0.1 ................................... [CV] ....... kernel=linear, gamma=1, C=0.1, score=0.962, total= 0.1s [CV] kernel=rbf, gamma=1, C=1000 ..................................... [CV] ......... kernel=rbf, gamma=1, C=1000, score=0.637, total= 0.0s [CV] kernel=rbf, gamma=1, C=1000 ..................................... [CV] ......... kernel=rbf, gamma=1, C=1000, score=0.637, total= 0.0s [CV] kernel=rbf, gamma=1, C=1000 ..................................... [CV] ......... kernel=rbf, gamma=1, C=1000, score=0.625, total= 0.0s [CV] kernel=rbf, gamma=1, C=1000 ..................................... [CV] ......... kernel=rbf, gamma=1, C=1000, score=0.633, total= 0.0s [CV] kernel=rbf, gamma=1, C=1000 ..................................... [CV] ......... kernel=rbf, gamma=1, C=1000, score=0.633, total= 0.0s [CV] kernel=linear, gamma=1, C=10 .................................... [CV] ........ kernel=linear, gamma=1, C=10, score=0.938, total= 3.6s [CV] kernel=linear, gamma=1, C=10 .................................... [CV] ........ kernel=linear, gamma=1, C=10, score=0.938, total= 1.2s [CV] kernel=linear, gamma=1, C=10 .................................... [CV] ........ kernel=linear, gamma=1, C=10, score=1.000, total= 1.8s [CV] kernel=linear, gamma=1, C=10 .................................... [CV] ........ kernel=linear, gamma=1, C=10, score=0.949, total= 2.9s [CV] kernel=linear, gamma=1, C=10 .................................... [CV] ........ kernel=linear, gamma=1, C=10, score=0.987, total= 3.5s [CV] kernel=rbf, gamma=0.1, C=0.1 .................................... [CV] ........ kernel=rbf, gamma=0.1, C=0.1, score=0.637, total= 0.0s [CV] kernel=rbf, gamma=0.1, C=0.1 .................................... [CV] ........ kernel=rbf, gamma=0.1, C=0.1, score=0.637, total= 0.0s [CV] kernel=rbf, gamma=0.1, C=0.1 .................................... [CV] ........ kernel=rbf, gamma=0.1, C=0.1, score=0.625, total= 0.0s [CV] kernel=rbf, gamma=0.1, C=0.1 .................................... [CV] ........ kernel=rbf, gamma=0.1, C=0.1, score=0.633, total= 0.0s [CV] kernel=rbf, gamma=0.1, C=0.1 .................................... [CV] ........ kernel=rbf, gamma=0.1, C=0.1, score=0.633, total= 0.0s [CV] kernel=rbf, gamma=1, C=0.1 ...................................... [CV] .......... kernel=rbf, gamma=1, C=0.1, score=0.637, total= 0.0s [CV] kernel=rbf, gamma=1, C=0.1 ...................................... [CV] .......... kernel=rbf, gamma=1, C=0.1, score=0.637, total= 0.0s [CV] kernel=rbf, gamma=1, C=0.1 ...................................... [CV] .......... kernel=rbf, gamma=1, C=0.1, score=0.625, total= 0.0s [CV] kernel=rbf, gamma=1, C=0.1 ...................................... [CV] .......... kernel=rbf, gamma=1, C=0.1, score=0.633, total= 0.0s [CV] kernel=rbf, gamma=1, C=0.1 ...................................... [CV] .......... kernel=rbf, gamma=1, C=0.1, score=0.633, total= 0.0s [CV] kernel=linear, gamma=1, C=1000 .................................. [CV] ...... kernel=linear, gamma=1, C=1000, score=0.950, total= 7.6s [CV] kernel=linear, gamma=1, C=1000 .................................. [CV] ...... kernel=linear, gamma=1, C=1000, score=0.950, total= 2.0s [CV] kernel=linear, gamma=1, C=1000 .................................. [CV] ...... kernel=linear, gamma=1, C=1000, score=1.000, total= 2.6s [CV] kernel=linear, gamma=1, C=1000 .................................. [CV] ...... kernel=linear, gamma=1, C=1000, score=0.949, total= 7.3s [CV] kernel=linear, gamma=1, C=1000 .................................. [CV] ...... kernel=linear, gamma=1, C=1000, score=0.987, total= 7.7s [CV] kernel=rbf, gamma=0.001, C=0.1 .................................. [CV] ...... kernel=rbf, gamma=0.001, C=0.1, score=0.637, total= 0.0s [CV] kernel=rbf, gamma=0.001, C=0.1 .................................. [CV] ...... kernel=rbf, gamma=0.001, C=0.1, score=0.637, total= 0.0s [CV] kernel=rbf, gamma=0.001, C=0.1 .................................. [CV] ...... kernel=rbf, gamma=0.001, C=0.1, score=0.625, total= 0.0s [CV] kernel=rbf, gamma=0.001, C=0.1 .................................. [CV] ...... kernel=rbf, gamma=0.001, C=0.1, score=0.633, total= 0.0s [CV] kernel=rbf, gamma=0.001, C=0.1 .................................. [CV] ...... kernel=rbf, gamma=0.001, C=0.1, score=0.633, total= 0.0s [CV] kernel=rbf, gamma=0.0001, C=10 .................................. [CV] ...... kernel=rbf, gamma=0.0001, C=10, score=0.950, total= 0.0s [CV] kernel=rbf, gamma=0.0001, C=10 .................................. [CV] ...... kernel=rbf, gamma=0.0001, C=10, score=0.912, total= 0.0s [CV] kernel=rbf, gamma=0.0001, C=10 .................................. [CV] ...... kernel=rbf, gamma=0.0001, C=10, score=0.975, total= 0.0s [CV] kernel=rbf, gamma=0.0001, C=10 .................................. [CV] ...... kernel=rbf, gamma=0.0001, C=10, score=0.949, total= 0.0s [CV] kernel=rbf, gamma=0.0001, C=10 .................................. [CV] ...... kernel=rbf, gamma=0.0001, C=10, score=0.949, total= 0.0s [CV] kernel=rbf, gamma=0.01, C=1000 .................................. [CV] ...... kernel=rbf, gamma=0.01, C=1000, score=0.637, total= 0.0s [CV] kernel=rbf, gamma=0.01, C=1000 .................................. [CV] ...... kernel=rbf, gamma=0.01, C=1000, score=0.637, total= 0.0s [CV] kernel=rbf, gamma=0.01, C=1000 .................................. [CV] ...... kernel=rbf, gamma=0.01, C=1000, score=0.613, total= 0.0s [CV] kernel=rbf, gamma=0.01, C=1000 .................................. [CV] ...... kernel=rbf, gamma=0.01, C=1000, score=0.633, total= 0.0s [CV] kernel=rbf, gamma=0.01, C=1000 .................................. [CV] ...... kernel=rbf, gamma=0.01, C=1000, score=0.633, total= 0.0s
[Parallel(n_jobs=1)]: Done 50 out of 50 | elapsed: 41.0s finished
Out[23]:
RandomizedSearchCV(estimator=SVC(),
param_distributions={'C': [0.1, 1, 10, 100, 1000],
'gamma': [1, 0.1, 0.01, 0.001, 0.0001],
'kernel': ['rbf', 'linear']},
verbose=3)
In [24]:
# print best parameter after tuning print(rdm.best_params_) # print how our model looks after hyper-parameter tuning print(rdm.best_estimator_)
{'kernel': 'linear', 'gamma': 1, 'C': 1000}
SVC(C=1000, gamma=1, kernel='linear')
In [25]:
rdm_predictions = rdm.predict(X_test) # print classification report print(classification_report(y_test, rdm_predictions))
precision recall f1-score support
0 0.97 0.89 0.93 66
1 0.94 0.98 0.96 105
accuracy 0.95 171
macro avg 0.95 0.94 0.94 171
weighted avg 0.95 0.95 0.95 171
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