Handling missing values in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What are missing values in a dataset?

Missing values are data points that are not recorded or are absent in a dataset. They can happen due to errors, skipped questions, or data loss.

beginner

Name two simple methods to handle missing values.

Two simple methods are: 1) Removing rows or columns with missing values, 2) Filling missing values with a fixed value like mean, median, or mode.

intermediate

Why is it sometimes better to fill missing values instead of removing them?

Filling missing values keeps more data for learning, which can improve model accuracy. Removing too many rows or columns can lose important information.

beginner

What is mean imputation?

Mean imputation replaces missing values in a numeric column with the average (mean) of the available values in that column.

intermediate

What is a potential downside of using mean imputation?

Mean imputation can reduce data variability and may bias the model because it ignores the natural spread of data and relationships between features.

What does 'dropping missing values' mean?

A Replacing missing values with zeros

B Removing rows or columns that contain missing data

C Ignoring missing values during training

D Predicting missing values using a model

Dropping missing values means removing rows or columns that have missing data to avoid problems during analysis.

Which method fills missing numeric values with the average of the column?

A Mean imputation

B Median imputation

C Mode imputation

D Forward fill

Mean imputation replaces missing numeric values with the average (mean) of the available values.

Why might removing all rows with missing values be a bad idea?

A It fills missing values automatically

B It always improves model accuracy

C It can cause loss of too much data

D It changes the data types

Removing rows with missing values can cause loss of important data, reducing the amount of information available for learning.

Which of these is NOT a common way to fill missing values?

A Using mean or median

B Using mode for categorical data

C Using random noise

D Dropping the entire dataset

Dropping the entire dataset is not a method to handle missing values; it would discard all data.

What is a risk of using mean imputation?

A It can bias the model by reducing variability

B It removes all missing values permanently

C It increases data variability

D It only works for categorical data

Mean imputation reduces variability by replacing missing values with the same average, which can bias the model.

Explain why handling missing values is important before training a machine learning model.

Think about what happens if the model sees empty spots in data.

Describe two common techniques to handle missing values and when you might use each.

Consider small vs large amounts of missing data.

Practice

1. What is the main reason to handle missing values before training a machine learning model?

easy

A. Missing values are automatically ignored by all models

B. Missing values can cause errors or reduce model accuracy

C. Missing values improve model training speed

D. Missing values increase the size of the dataset

Solution

1. Step 1: Understand the impact of missing values

   Missing values can cause errors or confuse the model, leading to poor predictions.

2. Step 2: Recognize model requirements

   Most machine learning models require complete data without missing values to work properly.

3. Final Answer:

   Missing values can cause errors or reduce model accuracy -> Option B

4. Quick Check:

   Missing values harm model accuracy = D [OK]

Hint: Missing data breaks models, so fix it first [OK]

Common Mistakes:

• Thinking missing values speed up training
• Assuming models ignore missing data automatically
• Believing missing values increase dataset size

2. Which of the following is the correct way to import and use SimpleImputer from scikit-learn to fill missing values with the mean?

easy

A. from sklearn.impute import SimpleImputer\nimputer = SimpleImputer(method='mean')

B. import SimpleImputer from sklearn.impute\nimputer = SimpleImputer('mean')

C. from sklearn.imputer import SimpleImputer\nimputer = SimpleImputer(strategy=mean)

D. from sklearn.impute import SimpleImputer\nimputer = SimpleImputer(strategy='mean')

Solution

1. Step 1: Check import syntax

   The correct import is 'from sklearn.impute import SimpleImputer'.

2. Step 2: Check SimpleImputer initialization

   The strategy parameter must be a string like 'mean', so strategy='mean' is correct.

3. Final Answer:

   from sklearn.impute import SimpleImputer\nimputer = SimpleImputer(strategy='mean') -> Option D

4. Quick Check:

   Correct import and strategy syntax = A [OK]

Hint: Use strategy='mean' and correct import syntax [OK]

Common Mistakes:

• Using 'method' instead of 'strategy'
• Not quoting 'mean' as a string
• Wrong import statement syntax

3. Given the following code, what will be the output of print(X_imputed)?

import numpy as np
from sklearn.impute import SimpleImputer

X = np.array([[1, 2], [np.nan, 3], [7, np.nan]])
imputer = SimpleImputer(strategy='mean')
imputer.fit(X)
X_imputed = imputer.transform(X)
print(X_imputed)

medium

A. [[1. 2.]
[4. 3.]
[7. 2.5]]

B. [[1. 2.]
[nan 3.]
[7. nan]]

C. [[1. 2.]
[4. 3.]
[7. nan]]

D. Error due to missing values

Solution

1. Step 1: Calculate mean ignoring missing values

   Column 0 mean = (1 + 7) / 2 = 4; Column 1 mean = (2 + 3) / 2 = 2.5

2. Step 2: Replace missing values with column means

   Replace np.nan in row 2, col 0 with 4; replace np.nan in row 3, col 1 with 2.5

3. Final Answer:

   [[1. 2.] [4. 3.] [7. 2.5]] -> Option A

4. Quick Check:

   Missing values replaced by column means = B [OK]

Hint: Mean replaces NaN with average of column values [OK]

Common Mistakes:

• Not calculating mean correctly
• Leaving NaNs unchanged
• Expecting an error instead of imputation

4. Identify the error in the following code snippet for imputing missing values:

from sklearn.impute import SimpleImputer

X_train = [[1, 2], [None, 3], [7, None]]
imputer = SimpleImputer(strategy='median')
imputer.transform(X_train)

medium

A. Using null instead of np.nan causes error

B. Incorrect strategy name 'median'

C. Missing fit() call before transform()

D. SimpleImputer cannot handle 2D lists

Solution

1. Step 1: Check method sequence

   SimpleImputer requires fit() before transform() to learn statistics from data.

2. Step 2: Verify data and strategy

   Strategy 'median' is valid; None is accepted as missing; 2D list is acceptable.

3. Final Answer:

   Missing fit() call before transform() -> Option C

4. Quick Check:

   fit() must precede transform() = A [OK]

Hint: Always fit before transform with SimpleImputer [OK]

Common Mistakes:

• Calling transform without fit
• Confusing strategy names
• Thinking None is invalid missing value

5. You have a training set with missing values and a test set with missing values in the same columns. What is the best practice to handle missing values before training and evaluating a model?

hard

A. Fit the imputer on training data, then transform both training and test data

B. Fit the imputer separately on training and test data

C. Fill missing values in test data with zeros, training data with mean

D. Remove all rows with missing values from both datasets

Solution

1. Step 1: Understand data leakage risk

   Fitting imputer separately on test data leaks information from test set, causing biased evaluation.

2. Step 2: Apply consistent imputation

   Fit imputer only on training data to learn statistics, then apply same transformation to test data.

3. Final Answer:

   Fit the imputer on training data, then transform both training and test data -> Option A

4. Quick Check:

   Fit on train, transform both = C [OK]

Hint: Fit imputer on train only, then transform test [OK]

Common Mistakes:

• Fitting imputer separately on test data
• Filling test missing values with zeros arbitrarily
• Dropping too much data unnecessarily