Polynomial regression in scikit-learn Question Title * 1. What does a negative correlation score between two features imply? We have an inverse correlation between data points, meaning when one feature increases, the other one decreases. There is no correlation between features We have a forward correlation between data points, meaning when one feature increases, the other does the same. Question Title * 2. What do polynomial features do? Allow faster learning. Transform our data to a higher dimensional space. Increase number of features. Change the algorithm structure. Question Title * 3. What is the name of class which transforms our features to polynomial PolynomialFeatures PolyFeatures pol2D Poly Question Title * 4. What is a C parameter in terms of converting features to polynomial using scikit-learn? A degree of polynomial. Number of random features to be transformed. Number of first features to be transformed. A power of regularization. Question Title * 5. Do we need to apply polynomial transformation to ground truth labels (y) while applying them to features? Yes, as we need to have labels in the same format as features. No, we don’t need to apply any transformations to labels. Yes, as it will make our model more accurate. Question Title * 6. How do we predict with a linear regression trained on polynomial features?model - trained model Cast input data to polynomial using the polynomial features transformer, then model.inference on transformed input data. Just use model.inference on input data without any transformations. Just use model.predict on input data without any transformations. Cast input data to polynomial using the polynomial features transformer, then model.predict on transformed input data. Question Title * 7. What is the result of execution this line of code df.iloc[0]['PTRATIO'] after boston dataset upload? 18.7 16.8 17.8 15.3 Готово