グリッドサーチによる機械学習モデルのチューニング

グリッドサーチを使ったハイパーパラメータのチューニング

Coding - サポートベクタマシンのパイプランのトレーニングとチューニング

code: Python

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import LabelEncoder

# yのカテゴリ変数「M」「B」を数値に変換する

le = LabelEncoder()

y = le.fit_transform(y)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, stratify=y, random_state=1)

from sklearn.pipeline import make_pipeline

from sklearn.preprocessing import StandardScaler

from sklearn.model_selection import GridSearchCV

from sklearn.svm import SVC

pipe_svc = make_pipeline(StandardScaler(), SVC(random_state=1))

gs = GridSearchCV(estimator=pipe_svc, param_grid=param_grid, scoring='accuracy', cv=10, n_jobs=-1)

gs = gs.fit(X_train, y_train)

print(gs.best_score_)

print(gs.best_params_)

clf = gs.best_estimator_

clf.fit(X_train, y_train)

print('Test accuarcy: {}'.format(clf.score(X_test, y_test)))

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0.9846153846153847

{'svc__C': 100.0, 'svc__gamma': 0.001, 'svc__kernel': 'rbf'}

Test accuarcy: 0.9736842105263158

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入れ子式の交差検証によるアルゴリズムの選択

Coding - SVMと決定木を比較する

code: Python

import numpy as np

from sklearn.model_selection import cross_val_score

gs = GridSearchCV(estimator=pipe_svc, param_grid=param_grid, scoring='accuracy', cv=2)

scores = cross_val_score(gs, X_train, y_train, scoring='accuracy', cv=5)

print('CV accuracy: {:.3f} +/- {:.3f}'.format(np.mean(scores), np.std(scores)))

from sklearn.tree import DecisionTreeClassifier

scores = cross_val_score(gs, X_train, y_train, scoring='accuracy', cv=5)

print('CV accuracy: {:.3f} +/- {:.3f}'.format(np.mean(scores), np.std(scores)))

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CV accuracy: 0.974 +/- 0.015

CV accuracy: 0.934 +/- 0.016

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