填充缺失值的三種方法

Completing a numerical continuous feature

Now we should start estimating and completing features with missing or null values. We will first do this for the Age feature.

We can consider three methods to complete a numerical continuous feature.

A simple way is to generate random numbers between mean and standard deviation.

More accurate way of guessing missing values is to use other correlated features. In our case we note correlation among Age, Gender, and Pclass. Guess Age values using median values for Age across sets of Pclass and Gender feature combinations. So, median Age for Pclass=1 and Gender=0, Pclass=1 and Gender=1, and so on...

Combine methods 1 and 2. So instead of guessing age values based on median, use random numbers between mean and standard deviation, based on sets of Pclass and Gender combinations.

Method 1 and 3 will introduce random noise into our models. The results from multiple executions might vary. We will prefer method 2.