How do I validate my machine learning model in Python?

How do I validate my machine learning model in Python?

You can validate your machine learning model using techniques like cross-validation, train-test split, and metrics evaluation.

What is cross-validation and how do I implement it?

Cross-validation is a technique to assess how the results of a statistical analysis will generalize to an independent dataset. In Python, you can use KFold or StratifiedKFold from the sklearn.model_selection module.

Can you show me a code example for KFold cross-validation?

Sure! Here's a simple example:

from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris

# Load dataset
X, y = load_iris(return_X_y=True)

# Initialize KFold
kf = KFold(n_splits=5)

for train_index, test_index in kf.split(X):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    model = LogisticRegression()
    model.fit(X_train, y_train)
    print(model.score(X_test, y_test))

What metrics should I use to evaluate my model's performance?

Common metrics include accuracy, precision, recall, F1 score, and ROC-AUC. The choice depends on your specific problem.