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from sklearn.model_selection import GridSearchCV
from sklearn.metrics import confusion_matrix, precision_score, recall_score, accuracy_score, f1_score
import numpy as np
def my_evaluation(y, pred):
# 正解と予測値を入れると自動で評価してくれます。
print('混同行列:')
print(confusion_matrix(y, pred))
accuracy = accuracy_score(y, pred)
print("正解率: %.3f" % accuracy)
if len(np.unique(y))==2:
precision = precision_score(y, pred)
recall = recall_score(y, pred)
f1 = f1_score(y, pred)
print("精度: %.3f" % precision)
print("再現率: %.3f" % recall)
print("F1スコア: %.3f" % f1)
# 万能関数
def my_model(X_train, X_test, y_train, y_test, model, name=''):
# モデル訓練
model.fit(X_train, y_train)
pred = model.predict(X_test)# 予測
# モデル評価
print(f'モデル名:{name}')
my_evaluation(y_test, pred)
return model
def my_best_model(X_train, X_test, y_train, y_test, model,params, name=''):
# モデル訓練
# 基礎モデル、パラメーター集合
models = GridSearchCV(model, params)
models.fit(X_train, y_train)
best_model = models.best_estimator_
pred = best_model.predict(X_test)# 予測
# モデル評価
print(f'モデル名:{name}')
my_evaluation(y_test, pred)
return best_model
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