【Advanced】Basic Machine Learning in MATLAB: Classification and Regression
发布时间: 2024-09-13 16:49:23 阅读量: 8 订阅数: 26
# [Advanced篇] Introduction to Machine Learning in MATLAB: Classification and Regression
MATLAB is a powerful computational environment that offers a wide range of tools to support machine learning tasks. Machine learning is a form of artificial intelligence that enables computers to learn from data without explicit instructions. The MATLAB machine learning toolbox provides various algorithms and functions, allowing you to easily build, train, and evaluate machine learning models.
This tutorial will guide you through machine learning in MATLAB. We will cover topics ranging from classification and regression algorithms to model evaluation and optimization. By the end of this tutorial, you will have the foundational knowledge and skills required for machine learning in MATLAB.
# 2. Classification Algorithms
### 2.1 Linear Classifiers
Linear classifiers are algorithms that categorize data points into different classes using linear equations to model the data. In MATLAB, two commonly used linear classifiers are the perceptron and logistic regression.
#### 2.1.1 Perceptron
The perceptron is a simple binary classification algorithm that separates data points into two classes using a hyperplane. The hyperplane is defined by the following linear equation:
```
w^T x + b = 0
```
Where:
* w is the weight vector
* x is the data point
* b is the bias term
The perceptron algorithm learns the hyperplane by iteratively updating the weight vector w and the bias term b. During each iteration, the algorithm checks if the data points are correctly classified. If a data point is misclassified, the algorithm updates the weight vector and bias term to bring the hyperplane closer to the data point.
**Code Block:**
```
% Create data
X = [1, 2; 3, 4; 5, 6; 7, 8];
y = [1; 1; -1; -1];
% Create a perceptron object
perceptron = perceptron;
% Train the perceptron
perceptron.train(X, y);
% Predict using the perceptron
predictions = perceptron.predict(X);
% Evaluate the perceptron
accuracy = sum(predictions == y) / length(y);
disp(['Accuracy: ', num2str(accuracy)]);
```
**Logical Analysis:**
* `perceptron = perceptron` creates a perceptron object.
* `perceptron.train(X, y)` trains the perceptron with data X and labels y.
* `perceptron.predict(X)` makes predictions with the perceptron on data X.
* `accuracy = sum(predictions == y) / length(y)` calculates the perceptron's accuracy.
#### 2.1.2 Logistic Regression
Logistic regression is a linear classifier used for binary classification. It maps data points to the probability space using a logistic function and then uses maximum likelihood estimation to learn the model parameters.
**Code Block:**
```
% Create data
X = [1, 2; 3, 4; 5, 6; 7, 8];
y = [1; 1; -1; -1];
% Create a logistic regression object
logisticRegression = logisticRegression;
% Train logistic regression
logisticRegression.train(X, y);
% Predict using logistic regression
predictions = logisticRegression.predict(X);
% Evaluate logistic regression
accuracy = sum(predictions == y) / length(y);
disp(['Accuracy: ', num2str(accuracy)]);
```
**Logical Analysis:**
* `logisticRegression = logisticRegression` creates a logistic regression object.
* `logisticRegression.train(X, y)` trains logistic regression with data X and labels y.
* `logisticRegression.predict(X)` makes predictions with logistic regression on data X.
* `accuracy = sum(predictions == y) / length(y)` calculates logistic regression's accuracy.
# 3. Regression Algorithms
Regression algorithms are used to predict continuous target variables. MATLAB offers a variety of regression algorithms to solve different types of problems. This chapter will introduce linear and nonlinear regression algorithms.
### 3.1 Linear Regression
Linear regression is a simple yet effective algorithm for predicting continuous target variables. It assumes a linear relationship between the target variable and input features.
#### 3.1.1 Ordinary Least Squares (OLS)
Ordinary least squares (OLS) is the most commonly used method in linear regression. It finds the best-fitting line by minimizing the squared error.
```matlab
% Input data
X = [1, 2, 3, 4, 5]';
y = [2, 4, 6, 8, 10]';
% Train the model
model = fitlm(X, y);
% Predict new data
y_pred = predict(model, [6]');
% Calculate Mean Squared Error (MSE)
mse = mean((y_pred - y(end))^2);
```
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