Statistical Data Mining

Question 1

Loss

Answer 1

is a numerical metric that describes how wrong a model’s predictions are. Loss measures the distance between the model’s predictions and the actual labels

Question 2

Linear Regression

Answer 2

is a statistical technique used to find the relationship between variables. In an ML context, linear regression finds the relationship between features and a label.

Question 3

How do you decide between Mean Absolute Error and Mean Standard Errors?

Answer 3

Choosing between MAE and MSE depends on the dataset and how you want the model to handle outliers, with MSE penalizing them more heavily.

Question 4

The prediction formula for linear regression model

Answer 4

Prediction = Bias + (Weight × Feature value)
the weight is the coefficient or multiplier used in the linear model
The feature value is the actual data point for the input variable you’re using.

Question 5

y’ = b + w1x1
bias

Answer 5

Bias is the same concept as the y-intercept in the algebraic equation for a line. In ML, bias is sometimes referred to as
. Bias is a parameter of the model and is calculated during training.

Question 6

y’ = b + w1x1
weight

Answer 6

w1 Weight is the same concept as the slope
in the algebraic equation for a line. Weight is a parameter of the model and is calculated during training.

Question 7

y’ = b + w1x1
feature

Answer 7

x1 is a feature—the input.