2.Low Variance Filter As a result, you build 5 GBM models, thinking a boosting algorithm would do the magic. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall. What’s about it? In order to retain those variables, we can use penalized regression models like ridge or lasso regression. 25. Answer: If you have worked on enough data sets, you should deduce that cancer detection results in imbalanced data. On the other hand, euclidean metric can be used in any space to calculate distance. On the contrary, stratified sampling helps to maintain the distribution of target variable in the resultant distributed samples also. Explain machine learning to me like a 5 year old. You should always find this out prior to beginning your interview preparation. When intercept term is present, R² value evaluates your model wrt. How do you select rows from dataframe? Measure information gain for the available set of features and select top n features accordingly. What is going on? A machine learning problem consist of three things: Always look for these three factors to decide if machine learning is a tool to solve a particular problem. Q32. 47. What would you do? You came to know that your model is suffering from low bias and high variance. In an imbalanced data set, accuracy should not be used as a measure of performance because 96% (as given) might only be predicting majority class correctly, but our class of interest is minority class (4%) which is the people who actually got diagnosed with cancer. 48. But, with growth in machine learning startups, facing off ML algorithm related question have higher chances, though I have laid emphasis on statistical modeling as well. Explain prior probability, likelihood and marginal likelihood in context of naiveBayes algorithm? Since, the data is spread across median, let’s assume it’s a normal distribution. Q3. The higher the threshold, the more aggressive the reduction. How do we interchange the values of two lists? Later, you tried a time series regression model and got higher accuracy than decision tree model. Therefore, it depends on our model objective. I believe the expressions for bias and variance in question 39 is incorrect. 77. What are the advantages of NumPy arrays over Python lists? Q4. Why? The intercept term shows model prediction without any independent variable i.e. They are practically only applicable to a data set with an already relatively low number of input columns. Your manager has asked you to run PCA. 76. What percentage of data would remain unaffected? Then, these samples are used to generate a set of models using a single learning algorithm. Get the data type of ‘points’ column from ‘reviews’ dataframe, Dropping columns ‘points’ and ‘country’ from ‘reviews’ dataframe, reviews.drop([‘points’, ‘country’], axis=1, inplace=True), Keeping columns ‘points’ and ‘country’ from ‘reviews’ dataframe, Rename ‘region_1’ as ‘region’ and ‘region_2’ as ‘locale’, reviews.rename(columns=dict(region_1=’region’, region_2=’locale’)). Answer: You should say, the choice of machine learning algorithm solely depends of the type of data.
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