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Scatter plot

A scatter plot shows the relationship between two variables with points.

Scatter plot

A scatter plot shows the relationship of two numeric variables with points. With the c parameter, points are coloured by class; in classification you immediately see whether the classes separate:

from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
fig, ax = plt.subplots()
sc = ax.scatter(X[:, 2], X[:, 3], c=y, cmap="viridis", s=20)
ax.set_xlabel("taç yaprak uzunluğu")
ax.set_ylabel("taç yaprak genişliği")
fig.colorbar(sc)

In Iris these two features separate the three species almost completely; this explains why the tree found them important.

Truth versus prediction

The clearest way to evaluate a regression model is to plot the true values against the predictions. In a good model the points gather around the y = x diagonal:

ax.scatter(y_test, p, alpha=0.6)
lim = [y_test.min(), y_test.max()]
ax.plot(lim, lim, "r--")   # perfect-prediction line

If the points pile up below the diagonal, the model is systematically under-predicting.

Practice Plot the classes

Draw columns 3 and 4 of Iris (X[:, 2], X[:, 3]) as a scatter plot with c=y and save it. Then compute each class's mean petal length with [X[y == k, 2].mean() for k in range(3)] and print it with np.round(..., 2).

Starter code

model.py
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)

# buraya yaz

Sample solution

model.py
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)

fig, ax = plt.subplots()
ax.scatter(X[:, 2], X[:, 3], c=y)
fig.savefig("iris.png")
print(np.round([X[y == k, 2].mean() for k in range(3)], 2))

Expected output

[1.46 4.26 5.55]

Questions and answers

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

1 Which parameter colours the points by class label?

  • s
  • alpha
  • marker
  • c Correct answer

Explanation: c takes the colour values; s sets the size and alpha the transparency.

2 Draw the points semi-transparent.

Question 
ax.scatter(x, y, ____=0.5)
Answer 
ax.scatter(x, y, alpha=0.5)

Explanation: alpha is between 0 (invisible) and 1 (opaque); it reveals overlapping points.

3 Match the parameters.

Term Meaning
c Colour values
s Point size
alpha Transparency
cmap Colour map

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