A classification problem in machine learning is a type of supervised learning task where the goal is to predict the category or class of a given data point based on its features.
The key components of a classification problem in machine learning include:
Features: These are the attributes or characteristics of the data points that help in making predictions. For example, in a flower classification problem, features could include petal length, petal width, and color. In a classification problem, the features should ideally be quantifiable.
Classes: These are the categories or labels that we want to predict. For instance, in the flower example, the classes could be "rose," "tulip," or "daisy."
Training Data: This is a labeled dataset that contains examples of data points along with their corresponding classes. The model learns from this data to understand the relationship between features and classes.
Model: This is the algorithm or method used to classify the data. Common models for classification include logistic regression, decision trees, and support vector machines.
Prediction: After training the model, it can make predictions on new, unseen data points by assigning them to one of the predefined classes based on their features.
Evaluation Metrics: These are used to assess the performance of the classification model. Common metrics include accuracy, precision, recall, and F1 score.
Similarity metrics are mathematical measures used to determine how alike two data points are based on their features: Euclidean Distance, Manhattan Distance, Cosine Similarity, Jaccard Similarity