Supervised Learning with Scikit-Learn

Supervised learning involves training a model using labeled data to make predictions or classify data points. Scikit-learn is a popular Python library for implementing supervised learning models.

In this lesson, you’ll learn how to build and evaluate supervised learning models using Scikit-learn.

Supervised learning uses labeled data, where each input has a corresponding output. The goal is to learn a mapping from inputs to outputs.

Common tasks include:

• Regression: Predicting continuous values, e.g., house prices.
• Classification: Predicting categories, e.g., spam or not spam.

Install Scikit-learn using pip:

pip install scikit-learn

Once installed, you can import it and start building models.

Linear regression is used to predict continuous values. Let’s build a simple linear regression model:

# Example: Linear Regression
from sklearn.linear_model import LinearRegression

# Sample data
X = [[1], [2], [3]]
y = [2, 4, 6]

model = LinearRegression()
model.fit(X, y)
print("Predicted value:", model.predict([[4]]))

Logistic regression is used for binary classification tasks, such as determining whether an email is spam or not.

# Example: Logistic Regression
from sklearn.linear_model import LogisticRegression

# Sample data
X = [[1], [2], [3]]
y = [0, 0, 1]

model = LogisticRegression()
model.fit(X, y)
print("Predicted class:", model.predict([[1.5]]))

Decision trees are versatile models that can handle both regression and classification tasks. Let’s build a simple decision tree classifier:

# Example: Decision Tree Classifier
from sklearn.tree import DecisionTreeClassifier

# Sample data
X = [[1], [2], [3]]
y = [0, 1, 0]

model = DecisionTreeClassifier()
model.fit(X, y)
print("Predicted class:", model.predict([[2.5]]))

Scikit-learn provides metrics to evaluate model performance:

• Mean Squared Error (MSE): For regression models.
• Accuracy: For classification models.

# Example: Evaluating a model
from sklearn.metrics import mean_squared_error

# Predictions
y_true = [2, 4, 6]
y_pred = [2.1, 3.9, 5.8]
print("MSE:", mean_squared_error(y_true, y_pred))

Always split your dataset into training and testing sets to evaluate model performance on unseen data:

# Example: Train-Test Split
from sklearn.model_selection import train_test_split

X = [[1], [2], [3], [4]]
y = [0, 1, 0, 1]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
print("Training data:", X_train)

Here are some mistakes to avoid:

• Not splitting data into training and testing sets.
• Overfitting the model by using overly complex algorithms.
• Using poorly labeled data for training.

In this lesson, you learned:

• How to build regression and classification models using Scikit-learn.
• How to evaluate model performance using metrics like MSE and accuracy.
• The importance of splitting data into training and testing sets.
• Common mistakes to avoid in supervised learning tasks.

Great job! Let’s move to the next topic.