Supervised learning is a type of machine learning where the algorithm learns from labeled training data. This tutorial will guide you through the basics of supervised learning in Python.

Key Concepts

  • Training Data: Data that is used to train the model.
  • Test Data: Data that is used to evaluate the model's performance.
  • Features: The input variables used to train the model.
  • Labels: The output variable that the model is trying to predict.

Common Algorithms

  • Linear Regression: Used for predicting continuous values.
  • Logistic Regression: Used for predicting binary outcomes.
  • Decision Trees: A tree-like model that makes decisions based on a set of rules.
  • Random Forest: An ensemble learning method that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.

Getting Started

To get started with supervised learning in Python, you can use libraries such as scikit-learn and TensorFlow.

from sklearn.linear_model import LinearRegression

# Create a linear regression model
model = LinearRegression()

# Fit the model with training data
model.fit(X_train, y_train)

# Predict the output for new data
y_pred = model.predict(X_test)

Further Reading

For more information on supervised learning in Python, you can visit the following resources:

Machine Learning