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Mean

The mean is found by dividing the sum of data by the number of elements.

Mean

The arithmetic mean is the sum of the values divided by their count: (x₁ + ... + xₙ) / n. It describes the "centre" of the data.

x = np.array([4, 8, 6, 2])
print(x.mean())        # 5.0
print(np.mean(x))      # the same

The mean is sensitive to outliers: the mean of [4, 8, 6, 2, 100] is 24 and represents no typical value.

Mean along an axis

In a two-dimensional array the axis parameter picks the direction: axis=0 computes per column (along the rows), axis=1 per row.

X = np.array([[1, 10],
              [3, 20],
              [5, 30]])
print(X.mean(axis=0))   # [ 3. 20.]  mean of each feature
print(X.mean(axis=1))   # [ 5.5 11.5 17.5]  mean of each example

In ML, feature means (axis=0) are used in scaling and missing-value filling.

Practice Feature means

Compute the column means of matrix X with axis=0 and print them.

Starter code

model.py
import numpy as np

X = np.array([[170, 60], [180, 80], [160, 70]])

# buraya yaz

Sample solution

model.py
import numpy as np

X = np.array([[170, 60], [180, 80], [160, 70]])

print(X.mean(axis=0))

Expected output

[170.  70.]

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 What is the mean of <code>[1, 2, 3, 10]</code>?

  • 10.0
  • 4.0 Correct answer
  • 2.5
  • 3.0

Explanation: (1 + 2 + 3 + 10) / 4 = 16 / 4 = 4.

2 Compute the mean of each column.

Question 
m = X.mean(axis=____)
Answer 
m = X.mean(axis=0)

Explanation: axis=0 reduces along the rows and gives one value per column.

3 Match the expressions.

Term Meaning
x.mean() Mean of all values
X.mean(axis=0) Column means
X.mean(axis=1) Row means
x.sum() / len(x) Mean by hand

All lessons in Beginner

1 What is ML?

  1. What is machine learning?
  2. Supervised and unsupervised
  3. Data and model
  4. Training and testing
  5. Feature and label notation
  6. Summary - ML fundamentals

2 NumPy basics

  1. What is NumPy?
  2. Creating arrays
  3. Array properties
  4. Array indexing
  5. Reshaping arrays
  6. Summary - NumPy basics

3 Pandas basics

  1. What is Pandas?
  2. Creating a DataFrame
  3. Column operations
  4. Selecting and filtering data
  5. Viewing first rows
  6. Summary - Pandas basics

4 Data preprocessing

  1. Data cleaning
  2. Missing data
  3. Data types
  4. Label encoding
  5. Data splitting
  6. Summary - Data preprocessing

5 Statistics basics

  1. Mean
  2. Median
  3. Standard deviation
  4. Variance
  5. Correlation
  6. Summary - Statistics

6 Linear algebra

  1. Vectors
  2. Matrices
  3. Matrix multiplication
  4. Transpose
  5. Identity matrix
  6. Summary - Linear algebra

7 Project: Data analysis

  1. Loading data
  2. Cleaning data
  3. Computing statistics
  4. Printing results

8 Regression

  1. What is regression?
  2. Linear regression
  3. Model training
  4. Prediction
  5. Error measurement
  6. Summary - Regression

9 KNN

  1. What is KNN?
  2. The K parameter
  3. Distance measurement
  4. Classification
  5. Model training
  6. Summary - KNN

10 Decision trees

  1. What is a decision tree?
  2. Splitting criterion
  3. Depth
  4. Classification
  5. Overfitting
  6. Summary - Decision trees

11 Classification

  1. What is classification?
  2. Binary classification
  3. Multi-class
  4. Accuracy
  5. Confusion matrix
  6. Summary - Classification

12 Model evaluation

  1. Train-test split
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Model evaluation

13 Project: Classification

  1. Preparing data
  2. Training model
  3. Prediction
  4. Evaluation

14 Scaling

  1. What is scaling?
  2. StandardScaler
  3. MinMaxScaler
  4. When to use?
  5. Pipeline
  6. Summary - Scaling

15 Feature engineering

  1. What is a feature?
  2. Feature selection
  3. Feature extraction
  4. Categorical features
  5. Numerical features
  6. Summary - Feature engineering

16 Model selection

  1. What is model selection?
  2. Cross-validation
  3. Grid search
  4. Score comparison
  5. Hyperparameter
  6. Summary - Model selection

17 Project: Regression

  1. Preparing data
  2. Training model
  3. Prediction
  4. Error measurement

18 Metrics

  1. What is a metric?
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Metrics

19 Data visualization

  1. What is visualization?
  2. Scatter plot
  3. Histogram
  4. Line plot
  5. Box plot
  6. Summary - Visualization

20 Beginner final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4