What is the best strategy for choosing evaluation metric ?

Any machine learning model has an evaluation stage. There are various metrics possible, however one must follow the below mentioned rules as one of the best strategies: Application level tradeoffs influence the ML level tradeoffs which in turn leads to multiple metrics. Always have one metric for optimising and for rest put some constraints. As…

How to evaluate word vectors ?

Word vectors whether derived from word2vec or glove or by using co-occurrence statistics, they need to be evaluated for performance reasons. This can be done in 2 major ways as mentioned below: Intrinsic ways are used when word vectors are build or evaluated for a specific or an intermediate┬ásubtask. Such evaluations are fast to compute…

Machine Learning Evaluation Metrics

The purpose of any Machine Learning algorithm is to predict right value/class for the unseen data. This is called generalization and ensuring this, in general, can be very tricky. This can depend on the algorithm being used for both supervised and unsupervised learning tasks. There are two things to consider in this process – the…

What are evaluation metrics for multi-class classification problem ?

Multi-class classification evaluation can be done in either of the following ways : Average precision of each class, average recall of each class treating classifier for each class as one vs all classifier. Average of accuracy of each class treating the classifier for each class as a one-vs-all classifier Sum of all true positive entries…