Learn by Coding | How to get Classification Accuracy in Python | Machine Learning in Python

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to get Classification Accuracy. #python #machinelearning #dataanalytics #datascience

Machine learning classification is a technique used to train a model to predict a categorical outcome, such as whether an email is spam or not, or whether a customer will buy a product or not. In order to evaluate the performance of a classification model, we use a metric called accuracy.

Accuracy is the proportion of correct predictions made by the model, out of all the predictions made. It is a commonly used metric for classification problems and is easy to understand.

To calculate the accuracy of a classification model in Python, we first need to split the data into training and testing sets. This is done using the "train_test_split" function from the "scikit-learn" library. The function takes in the data, as well as the percentage of the data that should be used for testing, and returns the training and testing sets.

Once the data is split, the model is trained on the training set using the "fit" method. This method takes in the training data and the model parameters. After the model is trained, it is used to make predictions on the test set using the "predict" method.

The final step is to compare the predicted values to the true values, and calculate the accuracy. The "scikit-learn" library provides a function called "accuracy_score" which can be used to calculate the accuracy of the model. This function takes in the true values and the predicted values, and returns the accuracy as a proportion (between 0 and 1).

It's important to note that accuracy is a good metric to use when the classes in the data are balanced, meaning there are roughly the same number of examples for each class. However, if the classes are imbalanced, accuracy might not be the best metric to use, because the model could achieve a high accuracy by simply predicting the majority class all the time. In this case, other metrics such as precision, recall or F1-score should be used instead.

Additionally, we should also repeat this process multiple times (with different random splits) and average the results to get a more robust estimate of the model's performance. This is known as k-fold cross validation.

In summary, to calculate the accuracy of a machine learning classification model in Python, we first split the data into training and testing sets, train the model on the training set, make predictions on the test set, and then compare the predicted values to the true values using the "accuracy_score" function. It's a commonly used metric for classification problems, but it's important to note that accuracy might not be the best metric to use when the classes in the data are imbalanced.

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