Model training consists of three major tasks: method selection, performance evaluation, and model tuning. Method selection: This decision is based on the following factors: Whether the data project involves labeled data (supervised learning)or unlabeled data (unsupervised learning) Type of data: numerical, continuous, or categorical; text data; image data; speech data; etc Size of the dataset Performance evaluation: Commonly used techniques are: Error analysis using confusion matrix: A confusion matrix is created with four categories – true positives, false positives, true negatives and false negatives. The following metrics are used to evaluate a confusion matrix: Precision (P) = TP/(TP + FP)… Read More