ML Quiz 8

A Pearson correlation between two variables is zero but, still their values can still be related to each other.

• True

• False

Process of extracting features from raw data is _____

• Feature Extraction

• Feature Engineering

Integer and floating-point data don't need a special encoding because they can be multiplied by a numeric weight.

• True

• False

Categorical features have a discrete set of possible values.

• True

• False

Models cannot multiply strings by the learned weights, that's why we can not use feature engineering to convert strings to numeric values.

• True

• False

if we incorporate index numbers (i.e. 1, 2, 3, 4) instead of string values or categorical data directly into our model, it won't impose any constraints that might be problematic.

• True

• False

Suppose that you had 1,000,000 different street names in your data set that you wanted to include as values for street_name.

Which method you will use?

• One hot encoding

• Sparse representation

• Multi hot encoding

In the above images, which of the following is/are examples of multi-collinear features?

• Features in Image 1

• Features in Image 2

• Features in Image 3

• Features in Image 1 & 2

• Features in Image 3 & 1

In the above images, suppose you have identified multi-collinear features. Which of the following action(s) would you perform next?

1. Remove both collinear variables.

2. Instead of removing both variables, we can remove only one variable.

3. Removing correlated variables might lead to loss of information. In order to retain those variables, we can use penalized regression models like ridge or lasso regression.

• Only 1

• Only 2

• Only 3

• Either 1 or 3

• Either 2 or 3

Suppose you are given 7 Scatter plots 1-7 (left to right) and you want to compare Pearson correlation coefficients between variables of each scatterplot.

Which of the following is in the right order?

1. 1<2<3<4

2. 1>2>3 > 4

3. 7<6<5<4

4. 7>6>5>4

• 1 and 3

• 2 and 3

• 1 and 4

• 2 and 4