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Summary - Visualization

In this group you learned scatter, histogram, and line plots.

Summary: visualisation

In this group:

  • fig, ax = plt.subplots(); drawing on the axis, savefig on the figure.
  • Scatter: relationship of two variables, classes with c=y; the truth-prediction plot.
  • Histogram: single-variable distribution, bins, np.histogram.
  • Line: validation and learning curves.
  • Box: quartiles, median, outliers.

Give every plot a title and axis names; the reader may not know the context.

Practice Two-panel figure

Open two axes with plt.subplots(1, 2): draw the histogram of x (bins=5) on the left and the scatter plot of y against x on the right, then save. Then print the correlation of x and y with round(..., 2).

Starter code

model.py
import numpy as np
import matplotlib.pyplot as plt

rng = np.random.default_rng(1)
x = rng.normal(0, 1, 200)
y = 2 * x + rng.normal(0, 1, 200)

# buraya yaz

Sample solution

model.py
import numpy as np
import matplotlib.pyplot as plt

rng = np.random.default_rng(1)
x = rng.normal(0, 1, 200)
y = 2 * x + rng.normal(0, 1, 200)

fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.hist(x, bins=5)
ax2.scatter(x, y)
fig.savefig("panel.png")
print(round(np.corrcoef(x, y)[0, 1], 2))

Expected output

0.9

Questions and answers

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

1 Which plot best shows a feature's outliers and the difference between classes at the same time?

  • A single line plot
  • A pie chart
  • A table
  • A box plot per class Correct answer

Explanation: Side-by-side boxes show medians, spread and outliers together.

2 Create two axes side by side.

Question 
fig, (ax1, ax2) = plt.subplots(1, ____)
Answer 
fig, (ax1, ax2) = plt.subplots(1, 2)

Explanation: subplots(rows, columns) produces a grid of axes.

3 Match the questions with the plots.

Term Meaning
Are two variables related? Scatter
How are the values distributed? Histogram
How does the score change? Line
How do the groups differ? Box

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