Scikit-learnでよく使う分類モデルまとめ

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Scikit-learnでよく使う分類モデルまとめ

モデルの評価

from sklearn.metrics import accuracy_score

accuracy_score(pred, val_y)

sklearnの分類モデル

ロジスティック回帰

from sklearn.linear_model import LogisticRegression

logreg = LogisticRegression()
logreg.fit(train_X, train_y)
pred = logreg.predict(val_X)

SVC

from sklearn.svm import SVC

svc = SVC()
svc.fit(train_X, train_y)
pred = svc.predict(val_X)

LinearSVC

from sklearn.svm import LinearSVC

linear_svc = LinearSVC()
linear_svc.fit(train_X, train_y)
pred = linear_svc.predict(val_X)

決定木

from sklearn.tree import DecisionTreeClassifier

decisiontree = DecisionTreeClassifier()
decisiontree.fit(train_X, train_y)
y_pred = decisiontree.predict(val_X)

ランダムフォレスト

from sklearn.ensemble import RandomForestClassifier

randomforest = RandomForestClassifier()
randomforest.fit(train_X, train_y)
pred = randomforest.predict(val_X)

KNeighborsClassifier

from sklearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier()
knn.fit(train_X, train_y)
pred = knn.predict(val_X)

ナイーブベイズ

from sklearn.naive_bayes import GaussianNB

gaussian = GaussianNB()
gaussian.fit(train_X, train_y)
pred = gaussian.predict(val_X)

GradientBoostingClassifier

from sklearn.ensemble import GradientBoostingClassifier

gbk = GradientBoostingClassifier()
gbk.fit(train_X, train_y)
pred = gbk.predict(val_X)

Stochastic Gradient Descent

from sklearn.linear_model import SGDClassifier

sgd = SGDClassifier()
sgd.fit(train_X, train_y)
pred = sgd.predict(val_X)

参考

1. Supervised learning — scikit-learn 0.21.2 documentation
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