FAQ on Machine Learning

What does NLP stand for?

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NLP stands for Natural Language Processing. It is a branch of artificial intelligence that gives machines the ability to read and understand human languages. NLP stands for Natural Language Processing. It is a field of artificial intelligence and linguistics concerned with the interactions between computers and humans (or natural languages) through natural language. NLP enables … Read more

Assume you need to generate a predictive model using multiple regression. Explain how you intend to validate this model

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There are two main ways that you can do this: A) Adjusted R-squared. R Squared is a measurement that tells you to what extent the proportion of variance in the dependent variable is explained by the variance in the independent variables. In simpler terms, while the coefficients estimate trends, R-squared represents the scatter around the … Read more

Explain what a false positive and a false negative are. Why is it important these from each other? Provide examples when false positives are more important than false negatives, false negatives are more important than false positives and when these two types of errors are equally important

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A false positive is an incorrect identification of the presence of a condition when it’s absent. A false negative is an incorrect identification of the absence of a condition when it’s actually present. An example of when false negatives are more important than false positives is when screening for cancer. It’s much worse to say … Read more

How to define/select metrics?

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There isn’t a one-size-fits-all metric. The metric(s) chosen to evaluate a machine learning model depends on various factors: Is it a regression or classification task? What is the business objective? Eg. precision vs recall What is the distribution of the target variable? There are a number of metrics that can be used, including adjusted r-squared, … Read more

What is cross-validation?

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Cross-validation is essentially a technique used to assess how well a model performs on a new independent dataset. The simplest example of cross-validation is when you split your data into two groups: training data and testing data, where you use the training data to build the model and the testing data to test the model. … Read more