Think Different

  Here are some of the most common performance metrics for machine learning: Confusion matrix : A confusion matrix is a table that summarizes the performance of a machine learning model. It shows the number of true positives, false positives, true negatives, and false negatives. True positive (TP): The model correctly predicts that the input is positive. False positive (FP): The model incorrectly predicts that the input is positive. True negative (TN): The model correctly predicts that the input is negative. False negative (FN): The model incorrectly predicts that the input is negative. Accuracy : Accuracy is the fraction of predictions that the model gets correct. It is calculated by dividing the number of correct predictions by the total number of predictions. Recall : Recall is the fraction of positive instances that the model correctly predicts. It is calculated by dividing the number of true positives by the sum of the true positives and false negatives. P...

  Inferential statistics is a branch of statistics that deals with making inferences about a population based on a sample. It is used in machine learning to make predictions about the performance of a model on new data. It is also used in performance testing to make inferences about the performance of a system under different workloads. In machine learning, inferential statistics is used to: Choose the right model: Inferential statistics can be used to evaluate different machine learning models and choose the one that is most likely to generalize well to new data. Tune the hyperparameters: The hyperparameters of a machine learning model are the parameters that control the model's behavior. Inferential statistics can be used to tune the hyperparameters of a model to improve its performance. Make predictions: Inferential statistics can be used to make predictions about the performance of a machine learning model on new data. In performance testing , inferential statistics is u...