Scikit-learn Classification

Train classifier
from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
model.fit(X_train, y_train)

Predictions
y_pred = model.predict(X_test)

Probability predictions
y_proba = model.predict_proba(X_test)

Train Random Forest
from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(
  n_estimators=100,
  random_state=42
)
model.fit(X_train, y_train)

Feature importance
importances = model.feature_importances_

Train SVM
from sklearn.svm import SVC

model = SVC(kernel="rbf")
model.fit(X_train, y_train)

Different kernels
svm_linear = SVC(kernel="linear")
svm_poly = SVC(kernel="poly", degree=3)

Accuracy
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)

Classification report
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))

Confusion matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

Cross-validation
from sklearn.model_selection import cross_val_score
scores = cross_val_score(model, X, y, cv=5)