python-scikit-learnBinary classification example
from sklearn import datasets, linear_model, model_selection
X, y = datasets.load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
model = linear_model.LogisticRegression(max_iter=10000)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)ctrl + cfrom sklearn importimport module from scikit-learn | model_selection.train_test_splitsplits given |
load_breast_cancerloads breast cancer dataset | .LogisticRegression(creates logistics regression model |
max_iterspecify maximum number of iterations for model training | .fit(train model with a given features and target variable dataset |
.predict(predict target variable based on given features dataset | |
Usage example
from sklearn import datasets, linear_model, model_selection
X, y = datasets.load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
model = linear_model.LogisticRegression(max_iter=10000)
model.fit(X_train, y_train)
print(model.score(X_test, y_test))output
0.958041958041958
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