Model Training

Model training is the core process of machine learning where an algorithm learns patterns from data to make predictions or decisions.

Steps in Model Training

1. Data Split: Divide data into training, validation, and test sets.
2. Model Selection: Choose an appropriate algorithm for your problem.
3. Hyperparameter Tuning: Optimize model parameters.
4. Model Fitting: Train the model on the training data.
5. Model Validation: Evaluate the model on the validation set.

Data Splitting

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Model Selection
from sklearn.ensemble import RandomForestClassifier

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

# Hyperparameter Tuning
from sklearn.model_selection import GridSearchCV

param_grid = {
    'n_estimators': [100, 200, 300],
    'max_depth': [None, 5, 10, 15]
}

grid_search = GridSearchCV(RandomForestClassifier(random_state=42), param_grid, cv=5)
grid_search.fit(X_train, y_train)

best_model = grid_search.best_estimator_

# Cross-validation
from sklearn.model_selection import cross_val_score

scores = cross_val_score(model, X_train, y_train, cv=5)
print(f"Cross-validation scores: {scores}")
print(f"Average score: {scores.mean()}")