Classification

What is Classification ?

Classification is a supervised learning technique used to predict the category or class of an object or data point based on its features. It is a fundamental task in machine learning where models are trained on labeled data to classify new, unseen data into predefined categories. The Classification tool allows users to compare the performance of different classifiers on various datasets, providing insights into which algorithm works best for specific use cases.

Features

• Multiple Classifier Support: Test and compare performance across different classification algorithms. • Dataset Flexibility: Works with diverse datasets from various domains. • Performance Metrics: Provides detailed accuracy, precision, recall, and F1-score for each classifier. • Visual Comparison: Presents results in a clear, understandable format for easy analysis. • Customizable Settings: Allows users to tweak parameters for specific use cases. • Export Results: Quickly export analysis for reports or further processing.

How to use Classification ?

1. Prepare Your Dataset: Ensure your data is labeled and formatted correctly.
2. Select Classifiers: Choose the algorithms you want to compare.
3. Train Models: Run the training process on your dataset.
4. Compare Performance: Analyze the results using provided metrics and visualizations.
5. Fine-Tune Models: Adjust parameters based on performance insights.
6. Export Results: Save or share your findings for further use.

Frequently Asked Questions

What is classification used for?
Classification is used for predicting categories or classes in data. Common applications include spam detection, sentiment analysis, and medical diagnosis.

What classifiers are supported?
Common classifiers like logistic regression, decision trees, random forests, and SVMs are typically supported.

How do I handle imbalanced datasets?
Techniques like resampling, adjusting class weights, or using algorithms robust to imbalance can help.