Data distributions and outliers in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the mean of the data list.

ML Python

mean_value = sum(data) / [1]

Drag options to blanks, or click blank then click option'

Amax(data)

Bsum(data)

Clen(data)

Dmin(data)

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum(data) instead of len(data) for the divisor.

Using max(data) or min(data) which are not counts.

The mean is the sum of all values divided by the number of values, which is given by len(data).

2fill in blank

medium

Complete the code to calculate the median of a sorted list named sorted_data.

ML Python

mid_index = len(sorted_data) // 2
median = sorted_data[[1]] if len(sorted_data) % 2 != 0 else (sorted_data[mid_index - 1] + sorted_data[mid_index]) / 2

Drag options to blanks, or click blank then click option'

Amid_index

B0

Clen(sorted_data)

Dmid_index + 1

Attempts:

3 left

💡 Hint

Common Mistakes

Using len(sorted_data) as index which is out of range.

Using mid_index + 1 which points to the wrong element.

The median for an odd-length list is the middle element at index mid_index.

3fill in blank

hard

Fix the error in the code to detect outliers using the interquartile range (IQR).

ML Python

iqr = q3 - q1
lower_bound = q1 - 1.5 * [1]
upper_bound = q3 + 1.5 * iqr
outliers = [x for x in data if x < lower_bound or x > upper_bound]

Drag options to blanks, or click blank then click option'

Aiqr

Bq1

Cq3

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using q3 or q1 directly instead of the IQR.

Using mean which is unrelated to IQR.

The IQR is used to calculate the bounds for outliers, so multiply 1.5 by iqr.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

ML Python

lengths = {word: [1] for word in words if len(word) [2] 3}

Drag options to blanks, or click blank then click option'

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself instead of its length.

Using less than < instead of greater than.

We want the length of each word, so use len(word), and filter words longer than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their lengths for words longer than 4 characters.

ML Python

result = { [1]: [2] for w in words if len(w) [3] 4}

Drag options to blanks, or click blank then click option'

Aw.upper()

Blen(w)

C>

Dw

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original word as key instead of uppercase.

Using less than < instead of greater than.

Using the word itself as value instead of length.

The keys are uppercase words using w.upper(), values are lengths with len(w), and filter words longer than 4 with >.

Practice

1. What is an outlier in a data distribution?

easy

A. A data point that is very different from most other points

B. The average value of all data points

C. The most common value in the data

D. A data point exactly in the middle of the dataset

Solution

1. Step 1: Understand what an outlier means

   An outlier is a point that stands out because it is far from the rest of the data.

2. Step 2: Compare options to definition

   Only A data point that is very different from most other points describes a point very different from others, which matches the outlier definition.

3. Final Answer:

   A data point that is very different from most other points -> Option A

4. Quick Check:

   Outlier = Different point [OK]

Hint: Outliers are points far from the main group [OK]

Common Mistakes:

• Confusing outlier with average
• Thinking outlier is the most common value
• Mixing outlier with median

2. Which of the following Python code snippets correctly calculates the interquartile range (IQR) to detect outliers?

easy

A. iqr = q3 - q1 # where q1 and q3 are 25th and 75th percentiles

B. iqr = mean + std # mean and standard deviation

C. iqr = max(data) - min(data) # range of data

D. iqr = median(data) * 2 # double the median

Solution

1. Step 1: Recall the IQR formula

   IQR is the difference between the 75th percentile (Q3) and 25th percentile (Q1).

2. Step 2: Match code to formula

   iqr = q3 - q1 # where q1 and q3 are 25th and 75th percentiles correctly subtracts q1 from q3. Other options use unrelated statistics.

3. Final Answer:

   iqr = q3 - q1 # where q1 and q3 are 25th and 75th percentiles -> Option A

4. Quick Check:

   IQR = Q3 - Q1 [OK]

Hint: IQR = Q3 minus Q1, not mean or median [OK]

Common Mistakes:

• Using mean or median instead of percentiles
• Confusing IQR with full data range
• Mixing standard deviation with IQR

3. Given the data points [5, 7, 8, 9, 10, 100], which value is most likely an outlier based on the IQR method?

medium

A. 7

B. 100

C. 10

D. 5

Solution

1. Step 1: Calculate Q1 and Q3

   Sorted data: [5,7,8,9,10,100]. Q1 (25th percentile) ≈ 7, Q3 (75th percentile) ≈ 9.5.

2. Step 2: Compute IQR and check outliers

   IQR = 9.5 - 7 = 2.5. Outliers are points below Q1 - 1.5*IQR = 7 - 3.75 = 3.25 or above Q3 + 1.5*IQR = 9.5 + 3.75 = 13.25. Only 100 is above 13.25, so it is an outlier.

3. Final Answer:

   100 -> Option B

4. Quick Check:

   100 > 13.25 means outlier [OK]

Hint: Outliers lie beyond Q1-1.5*IQR or Q3+1.5*IQR [OK]

Common Mistakes:

• Ignoring the large gap and picking smallest value
• Confusing max value as normal
• Not calculating IQR correctly

4. You have this Python code to detect outliers but it gives wrong results:

import numpy as np
q1 = np.percentile(data, 25)
q3 = np.percentile(data, 75)
iqr = q1 - q3
lower_bound = q1 - 1.5 * iqr
upper_bound = q3 + 1.5 * iqr
outliers = [x for x in data if x < lower_bound or x > upper_bound]

What is the error?

medium

A. Percentiles should be 50 and 100, not 25 and 75

B. Outlier condition should use 'and' instead of 'or'

C. IQR calculation is reversed; should be q3 - q1

D. Bounds calculation should add 1.5 to q1 and q3 directly

Solution

1. Step 1: Check IQR calculation

   IQR is the difference between Q3 and Q1, so it must be q3 - q1, not q1 - q3.

2. Step 2: Verify other parts

   Percentiles 25 and 75 are correct. Outlier condition uses 'or' correctly. Bounds use multiplication correctly.

3. Final Answer:

   IQR calculation is reversed; should be q3 - q1 -> Option C

4. Quick Check:

   IQR = Q3 - Q1, not reversed [OK]

Hint: IQR = Q3 minus Q1, watch subtraction order [OK]

Common Mistakes:

• Swapping q1 and q3 in IQR calculation
• Using wrong percentile values
• Confusing 'or' with 'and' in conditions

5. You have a dataset with many outliers skewing the mean. Which method is best to reduce outlier impact before training a machine learning model?

hard

A. Ignore outliers and train directly

B. Replace all values with the mean

C. Use the maximum value as the central measure

D. Remove data points outside 1.5 times the IQR range

Solution

1. Step 1: Understand outlier impact on mean

   Outliers can pull the mean away from the center, causing bias in training.

2. Step 2: Choose method to reduce outlier effect

   Removing points outside 1.5*IQR is a common way to remove outliers. Replacing with mean or ignoring outliers keeps bias. Using max value is not a central measure.

3. Final Answer:

   Remove data points outside 1.5 times the IQR range -> Option D

4. Quick Check:

   Remove outliers using IQR method [OK]

Hint: Cut off points beyond 1.5*IQR to reduce outliers [OK]

Common Mistakes:

• Replacing all data with mean loses information
• Using max value as center is wrong
• Ignoring outliers causes model bias