What is the difference between undercutting and overfitting?

 In the context of data science, you can address the underfitting by using techniques such has polynomial regression, decision trees with deeper splits, or even ensemble methods such as random forests or even gradient boosting.

On the other hand for the objective of tackling the overfitting, you can reduce the complexity of your particular model by regularisation techniques such as L1 or L2 regularisation.

Here is an example given in Python programming language by using scikit-learn:-

From sklearn.linear_model import Ridge
From sklearn.model_selection import train_test_split
From sklearn.metrics import mean_squared_error
# Assuming X contains the features and y contains the target variable (churn prediction)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train Ridge regression model with regularization parameter alpha

Ridge_model = Ridge(alpha=0.1)  # Adjust alpha as needed
Ridge_model.fit(X_train, y_train)

# Evaluate the model

Train_rmse = mean_squared_error(y_train, ridge_model.predict(X_train), squared=False)
Test_rmse = mean_squared_error(y_test, ridge_model.predict(X_test), squared=False)
Print(“Train RMSE:”, train_rmse)
Print(“Test RMSE:”, test_rmse)

You can address overfitting, you can add regularisation to the random forest classifier:-

From sklearn.ensemble import RandomForestClassifier
From sklearn.model_selection import train_test_split
From sklearn.metrics import accuracy_score

# Assuming X contains the features and y contains the target variable

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

# Create and train a Random Forest classifier with regularization parameters

# Adjust the max_depth and min_samples_split parameters to control overfitting

Rf_classifier = RandomForestClassifier(n_estimators=100, max_depth=10, min_samples_split=5, random_state=42)
Rf_classifier.fit(X_train, y_train)

# Evaluate the model

Train_accuracy = accuracy_score(y_train, rf_classifier.predict(X_train))
Test_accuracy = accuracy_score(y_test, rf_classifier.predict(X_test))
Print(“Train Accuracy:”, train_accuracy)
Print(“Test Accuracy:”, test_accuracy)