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2022-04-19 15:43:25 You’ve built a random forest model with 10000 trees. You got delighted after getting training error as 0.00. But, the validation error is 34.23. What is going on? Haven’t you trained your model perfectly?

Answer: The model has overfitted. Training error 0.00 means the classifier has mimiced the training data patterns to an extent, that they are not available in the unseen data. Hence, when this classifier was run on unseen sample, it couldn’t find those patterns and returned prediction with higher error. In random forest, it happens when we use larger number of trees than necessary. Hence, to avoid these situation, we should tune number of trees using cross validation. Open / Comment

2022-04-19 11:12:51 What are the problems with using trees for solving time series problems?

Random Forest models are not able to extrapolate time series data and understand increasing/decreasing trends. It will provide us with average data points if the validation data has values greater than the training data points.

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2022-04-18 10:00:08 Open / Comment

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2022-04-16 11:57:12 You are given a data set. The data set has missing values which spread along 1 standard deviation from the median. What percentage of data would remain unaffected? Why?

Answer: This question has enough hints for you to start thinking! Since, the data is spread across median, let’s assume it’s a normal distribution. We know, in a normal distribution, ~68% of the data lies in 1 standard deviation from mean (or mode, median), which leaves ~32% of the data unaffected. Therefore, ~32% of the data would remain unaffected by missing values. Open / Comment

2022-04-16 07:22:00 You have a series with only one variable “y” measured at time t. How do predict “y” at time t+1? Which approaches would you use?

We want to look at the correlation between different observations of y. This measure of correlation is called autocorrelation. Autoregressive models are multiple regression models where the time-lag series of the original time series are treated like multiple independent variables. Open / Comment

2022-04-16 06:36:03 Q) Can you compare the validation test with the test set?

Ans: Validation Dataset: The sample of data used to provide an unbiased evaluation of a model fit on the training dataset while tuning model hyperparameters. The evaluation becomes more biased as skill on the validation dataset is incorporated into the model configuration.

Test Dataset: The sample of data used to provide an unbiased evaluation of a final model fit on the training dataset. Open / Comment