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Data and model

Data is the learning source for ML algorithms; it consists of feature and label columns.

Data: rows and columns

ML data is a table. Each row is an example (a house, a customer) and each column is a feature (square metres, age). In Python this table is usually a two-dimensional NumPy array:

import numpy as np
X = np.array([[50, 1],
              [80, 2],
              [120, 3]])
print(X.shape)   # (3, 2): 3 examples, 2 features

scikit-learn always expects X to be two-dimensional; even with a single feature it is written as [[50], [80]].

Model: learned parameters

Training means adjusting the model's parameters to fit the data. In linear regression the parameters are the slope and the intercept; scikit-learn stores them in attributes ending with an underscore:

from sklearn.linear_model import LinearRegression
m = LinearRegression().fit([[1], [2], [3]], [3, 5, 7])
print(m.coef_, m.intercept_)   # [2.] 1.0  ->  y = 2x + 1

The trailing _ is a scikit-learn convention showing that the value was learned during fit.

Practice Read the parameters

Fit a LinearRegression to the given data. Round the slope (coef_[0]) and the intercept (intercept_) to integers with round and print them separated by a space.

Starter code

model.py
from sklearn.linear_model import LinearRegression

X = [[1], [2], [3], [4]]
y = [5, 8, 11, 14]

# buraya yaz

Sample solution

model.py
from sklearn.linear_model import LinearRegression

X = [[1], [2], [3], [4]]
y = [5, 8, 11, 14]

m = LinearRegression().fit(X, y)
print(round(m.coef_[0]), round(m.intercept_))

Expected output

3 2

Questions and answers

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

1 If <code>X.shape</code> is <code>(200, 5)</code>, how many examples are in the dataset?

  • 200 Correct answer
  • 5
  • 1000
  • 205

Explanation: The first dimension is the number of rows (examples), the second the number of columns (features).

2 Print the dimensions of the array.

Question 
X = np.array([[1, 2], [3, 4], [5, 6]])
print(X.____)
Answer 
X = np.array([[1, 2], [3, 4], [5, 6]])
print(X.shape)

Explanation: shape gives the (rows, columns) pair; here (3, 2).

3 Match the terms.

Term Meaning
row One example
column One feature
coef_ Learned slope
intercept_ Learned intercept

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