Applicare la regressione logistica e SVM

Classificatori lineari in Python

Michael (Mike) Gelbart

Instructor, The University of British Columbia

Uso di LogisticRegression

from sklearn.linear_model import LogisticRegression

lr = LogisticRegression()

lr.fit(X_train, y_train)

lr.predict(X_test)

lr.score(X_test, y_test)

Esempio con LogisticRegression

import sklearn.datasets
wine = sklearn.datasets.load_wine()

from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(wine.data, wine.target)

lr.score(wine.data, wine.target)

0.966

lr.predict_proba(wine.data[:1])

array([[9.966e-01, 2.740e-03, 6.787e-04]])

Uso di LinearSVC

LinearSVC funziona allo stesso modo:

import sklearn.datasets

wine = sklearn.datasets.load_wine()

from sklearn.svm import LinearSVC

svm = LinearSVC()

svm.fit(wine.data, wine.target)

svm.score(wine.data, wine.target)

0.955

Uso di SVC

import sklearn.datasets
wine = sklearn.datasets.load_wine()

from sklearn.svm import SVC
svm = SVC()
svm.fit(wine.data, wine.target);

svm.score(wine.data, wine.target)

0.708

Revisione della complessità del modello:

Underfitting: modello troppo semplice, bassa accuratezza in training
Overfitting: modello troppo complesso, bassa accuratezza in test

Esercitiamoci!

Classificatori lineari in Python