This brief video talks about how the ROC curve is constructed and how one can find the optimal threshold for a classifier such as logistic regression, from the ROC curve.
How to find the Optimal Threshold from ROC curve?
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Author: MLNerds
METEOR metric for machine translation
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This short video describes METEOR, a metric for evaluating machine generated text. It is used to evaluate whether the candidate text generated by an ML model matches the reference text (that is supposed to be generated). Where is the METEOR metric used? Meteor metric was primarily used in the Machine Translation literature. Checkout our article…
BLUE Score
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This brief video describes the BLEU score, a popular evaluation metric used for sevaral tasks such as machine translation, text summarization and so on. What is BLEU Score? BLEU stands for Bilingual evaluation Understudy. It is a metric used to evaluate the quality of machine generated text by comparing it with a reference text that…
Machine Translation
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Here is a high level overview of Machine Translation. This short video covers a brief history of machine translation followed by a quick explanation of SMT (statistical machine translation) and NMT (Neural Machine Translation) and a few resources to get started.
NDCG Evaluation Metric for Recommender Systems
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What is the NDCG metric? NDCG stands for Normalized Discounted Cumulative Gain. Recommender systems are important in sevaral application domains such as e-commerce, finance, healthcare and so on. It is important to come up with evaluation metrics to measure how well a recommender system works. Look at our article on Evaluation metrics for recommender systems…
LIME for local explanations
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This video talks about the LIME for local interpretablity. It explains the motivation for local explanations followed by how LIME works to provide local explanations along with information on where to find code examples and get started.
Global vs Local Interpretability
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This video explains why we need interpretability for our AI and the two approaches typically used for interpretability namely Global and Local Interpretability. While global interpretability can give global insights about the data, it cannot explain why the prediction for a specific data point is the way it is. Local interpretability models such as LIME…
MAP at K : An evaluation metric for Ranking
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This video talks about the Mean Average Precision at K (popularly called the MAP@K) metric that is commonly used for evaluating recommender systems and other ranking related problems. Why do we need Mean Average Precision@K metric? Traditional classification metrics such as precision, in the context of recommender systems can be used to see how many…
How to build a Global Surrogate Model for Interpretable AI?
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This brief 5 minute video explains building Global Surrogate Models for interpretable AI.
How do you use Complement Naive Bayes for Imbalanced Datasets?
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This brief video explains the Complement Naive Bayes classifier, a modification of the naive bayes classifier that works well for imbalanced datasets.