Accuracy: Difference between revisions

Accuracy in machine learning refers to a metric that measures the performance of a classification model. It measures the percentage of correct predictions made by the model on test data compared to all predictions made. Accuracy is one of the most frequently used metrics in machine learning and serves as a standard for comparing models' results.

==What is Accuracy?==

Accuracy is a measure of how well a machine learning model can correctly predict class labels from test data. It is defined as the ratio between correct predictions made by the model and all total predictions made.

However, when classes are imbalanced (one class with significantly more samples than the other), accuracy may not be an accurate measure of model performance. A model may achieve high accuracy by correctly predicting the majority class even if it performs poorly on the minority one. When dealing with imbalanced datasets, other metrics like precision, recall and F1 score may provide more insightful evaluations of model effectiveness.

Many factors can influence the accuracy of a classification model, such as its chosen algorithm, quality and quantity of training data, feature selection process, and hyperparameters used for tuning the model.

The choice of algorithm can significantly influence the accuracy of a model. Some algorithms may be better suited for specific data types or may perform better on small or large datasets. Furthermore, both quality and quantity of training data influence accuracy as models only learn patterns present in that data set.

The feature selection process is essential, as selecting relevant features can improve the model's accuracy. Finally, tuning the hyperparameters used to fine-tune the model has a significant effect on accuracy; selecting suitable hyperparameters will enhance performance overall.