binary-classification.md

Binary 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)

• from sklearn import - import module from lib:scikit-learn
• model_selection.train_test_split - splits given X and y datasets to test (25% of values by default) and train (75% of values by default) subsets
• load_breast_cancer - loads breast cancer dataset
• .LogisticRegression( - creates logistics regression model
• max_iter - specify 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

group: binary

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))

0.958041958041958