Linear Classifier

A linear classifier is a commonly used machine learning method for categorizing data points into distinct classes. It accomplishes classification tasks by constructing a linear decision boundary and is suitable for both binary and multi-class classification problems. MATLAB offers robust tools for implementing linear classifiers and supports visualization of the experimental process.

### Implementation Approach

Data Preparation First, it is necessary to prepare training and testing datasets. These datasets typically include feature vectors and their corresponding labels. In MATLAB, data can be stored in matrices where each row represents a sample and each column represents a feature.

Training the Classifier MATLAB provides several methods to implement linear classifiers, such as: `fitcsvm` (Support Vector Machine): Suitable for binary classification problems, allowing hyperparameter tuning to optimize model performance. `fitclinear` (Linear Classification Model): Ideal for high-dimensional data with high computational efficiency. `mnrfit` (Multinomial Logistic Regression): Designed for multi-class classification problems.

Model Evaluation After training, the classifier's performance can be evaluated using test data by calculating metrics like accuracy and confusion matrix. MATLAB's `predict` function can be used to classify new data points.

Visualizing Decision Boundaries By plotting data points and classification decision boundaries, the classification results can be visually inspected. The `gscatter` function can be used for scatter plot visualization, combined with the `contour` function to draw decision boundaries.

### Experimental Analysis

Experimental reports may include the following components: Dataset Description: Overview of data size, features, and class distribution. Model Selection: Explanation of classifier choice and parameter tuning process. Result Comparison: Performance comparison of different classifiers (e.g., accuracy, training time). Visualization Charts: Display of classification results for training/test data and decision boundary variations (e.g., under different classifiers or parameters).

MATLAB's advantage lies in its comprehensive built-in functions and plotting capabilities, making linear classifier implementation and validation highly efficient.