What are the various techniques and metrics used to evaluate how accurate / good an algorithm is? How would you use a given metric to derive a conclusion about a ML model?
Some possible methods:
Focus on the predictive capability of a model: confusion matrix (computing the accuracy, F1-score, cost of classification). You can draw an ROC curve, and performance of every classifier is represented as a point on the curve. When you change the threshold in the algorithm, sample distribution, or the cost matrix of classification, the point locations will change as well.
Learning curve (bias-variance tradeoff). It helps determine the sample size and the feature size.
Calculate the ratio between the predictive accuracy of the model and the baseline accuracy (without using your model). It is also called lift chart.