In which scenario is a classification model ideally used?

A classification model is best suited for scenarios where the goal is to categorize data into distinct classes. This involves assigning a label from a predefined set of categories based on the input features. For example, a classification model can be used to identify whether an email is spam or not, or to determine if an image contains a cat or a dog, thus reflecting the model's strength in handling discrete outcomes rather than continuous variables.

In contrast, predicting continuous outcomes or determining prices falls under regression tasks, where the output is a continuous value rather than a class label. Analyzing time-series data typically requires specialized models that capture temporal dependencies, like ARIMA or LSTM models, which are not inherently classification-oriented. The unique capability of classification models to handle distinct labels makes them ideal for classifying data points into specific, non-overlapping categories.