Rapid Fire on Machine Learning

I know that a linear regression model is generally evaluated using Adjusted R² or F value. How would you evaluate a logistic regression model?

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: We can use the following methods: Since logistic regression is used to predict probabilities, we can use AUC-ROC curve along with confusion matrix to determine its performance. Also, the analogous metric of adjusted R² in logistic regression is AIC. AIC is the measure of fit which penalizes model for the number of model coefficients. … Read more

Explain machine learning to me like a 5 year old.

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It’s simple. It’s just like how babies learn to walk. Every time they fall down, they learn (unconsciously) & realize that their legs should be straight and not in a bend position. The next time they fall down, they feel pain. They cry. But, they learn ‘not to stand like that again’. In order to … Read more

In k-means or kNN, we use euclidean distance to calculate the distance between nearest neighbors. Why not manhattan distance ?

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We don’t use manhattan distance because it calculates distance horizontally or vertically only. It has dimension restrictions. On the other hand, euclidean metric can be used in any space to calculate distance. Since, the data points can be present in any dimension, euclidean distance is a more viable option. Example: Think of a chess board, … Read more

You have been asked to evaluate a regression model based on R², adjusted R² and tolerance. What will be your criteria?

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Tolerance (1 / VIF) is used as an indicator of multicollinearity. It is an indicator of percent of variance in a predictor which cannot be accounted by other predictors. Large values of tolerance is desirable. We will consider adjusted R² as opposed to R² to evaluate model fit because R² increases irrespective of improvement in … Read more

You are working on a classification problem. For validation purposes, you’ve randomly sampled the training data set into train and validation. You are confident that your model will work incredibly well on unseen data since your validation accuracy is high. However, you get shocked after getting poor test accuracy. What went wrong?

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In case of classification problem, we should always use stratified sampling instead of random sampling. A random sampling doesn’t takes into consideration the proportion of target classes. On the contrary, stratified sampling helps to maintain the distribution of target variable in the resultant distributed samples also. If your model performs well on the validation set … Read more