# How to use Classification Metrics in Python

Classification Metrics are a set of techniques used to evaluate the performance of a classifier. These metrics provide a way to measure the accuracy, precision, recall and other aspects of a classifier’s performance. They are widely used in machine learning to evaluate the effectiveness of a model and make informed decisions about which model to use for a given problem.

In Python, the popular library scikit-learn provides a number of built-in functions for calculating classification metrics such as accuracy_score, precision_score, recall_score, f1_score, among others. Each of these functions calculates a different metric, and they can all be used to evaluate the performance of a classifier on a dataset.

Accuracy is the most common metric used to evaluate the performance of a classifier, it is the ratio of the number of correct predictions to the total number of predictions made.

Precision is the proportion of true positive predictions in relation to the total number of positive predictions made by the classifier.

Recall is the proportion of true positive predictions in relation to all the positive instances in the dataset.

F1 score is the harmonic mean of precision and recall.

In addition to these metrics, other useful classification metrics include AUC-ROC(Area Under Receiver Operating Characteristic) curve, confusion matrix and cross validation score.

AUC-ROC curve is a graphical representation of the performance of a classifier, which plots the true positive rate against the false positive rate.

Confusion Matrix is a table that is used to define the performance of a classification algorithm, it gives the number of true positives, false positives, true negatives and false negatives.

Cross-validation score is a technique for measuring the performance of a classifier by dividing the dataset into training and test subsets, and then training the classifier on the training set and evaluating it on the test set.

In conclusion, classification metrics are a set of techniques used to evaluate the performance of a classifier. Scikit-learn, a python library provide us with several built-in functions for calculating various classification metrics such as accuracy, precision, recall, f1-score, AUC-ROC and so on. These metrics allow us to evaluate the effectiveness of a model and make informed decisions about which model to use for a given problem.

In this Machine Learning Recipe, you will learn: How to use Classification Metrics in Python.

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