Lesson 1, Section 3: Introduction to Machine Learning
Supervised learning:
Supervised learning is a type of machine learning where the training data consists of labeled examples, meaning each data point has corresponding input features and known output labels. The goal is to build a model that can learn from the labeled data and make accurate predictions or classifications on new, unseen data points. Let’s dive deeper into supervised learning:
A. Training set: The training set is the labeled dataset used to train the supervised learning model. It consists of input features (X) and corresponding output labels (y). The model learns patterns and relationships within the training data to make predictions or classifications.
B. Test set: The test set is a separate dataset that is used to evaluate the performance of the trained model. It contains input features (X) but not the corresponding labels (y). By evaluating the model’s performance on unseen data, we can assess how well it generalizes and makes predictions on new instances.
C. Classification: Classification is a supervised learning task where the goal is to predict a discrete label or category for a given set of input features. The output labels are usually represented as class labels or categorical values. Common classification algorithms include logistic regression, support vector machines (SVM), decision trees, and random forests.
D. Regression: Regression is another supervised learning task that involves predicting a continuous value or numeric quantity. The output labels are continuous values. In regression, the model learns the relationship between the input features and the target variable. Linear regression, decision trees, random forests, and neural networks can be used for regression tasks.
During the training phase, the model adjusts its parameters or internal weights based on the labeled examples to minimize the error or difference between the predicted outputs and the actual labels. This process of adjusting the model’s parameters is often referred to as training or optimization.
Once the model is trained, it can be used to make predictions or classifications on new, unseen data points by applying the learned patterns and relationships.
Supervised learning is widely used in various domains, including image classification, natural language processing, recommendation systems, fraud detection, and more.