100 Facts About Classification Models

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Here’s a list of 100 facts about classification models:

1. Classification models are a type of supervised learning algorithm.
2. They are used for predicting the category or class labels of new, unseen instances.
3. Common types of classification models include logistic regression, decision trees, support vector machines, and neural networks.
4. Classification models are trained on a labeled dataset, where each example is associated with a known class label.
5. The output of a classification model is a categorical variable, representing the predicted class.
6. Binary classification models distinguish between two classes, while multiclass models classify instances into more than two classes.
7. Evaluation metrics for classification models include accuracy, precision, recall, F1 score, and area under the receiver operating characteristic (ROC) curve.
8. Confusion matrices are used to visualize the performance of a classification model.
9. True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) are components of a confusion matrix.
10. Sensitivity (Recall) measures the ability of a model to correctly identify positive instances.