Machine Learning Chapter 14: Data Visualization

What this chapter covers

This chapter contains 12 topics. Technical terms are followed by plain-language meanings in parentheses where they first appear. Code is included only when it naturally helps demonstrate the concept; architecture, workflow, governance, and comparison topics use practical scenarios instead.

14.1 Visualization Principles

Visualization Principles (a practical concept used within data collection, understanding, cleaning, and preparation). Within Chapter 14, this topic connects directly to data collection, understanding, cleaning, and preparation. The important goal is to understand what information goes into the method, what transformation or decision happens, and what output should be checked.

In a real project, this step can affect every model that comes later. Check data types, missing values, scale, categories, unusual records, and whether the transformation can be repeated consistently on new data. Good preparation reduces avoidable errors and helps make evaluation more trustworthy.

Example

Imagine a small real-world project where Visualization Principles is the main idea. Identify the input information, the decision or transformation that occurs, and the result you would inspect to decide whether the method is working correctly.

Coding example

// Visualization Principles
const records = [3, 5, 7, 9, 11];
const transform = value => ({ input: value, output: value * 2 + 1 });
const results = records.map(transform);

console.log(results);

Code explanation

The sample starts with a small list of inputs so every result can be checked manually.
`transform()` represents the main operation for this topic in a deliberately simple form.
`map()` applies the same rule consistently to every item and returns a new result array.
Use this pattern to focus on input, transformation, and output before replacing the toy rule with a more advanced method.

Expected result: A transformed result is printed for each input value.

Practice exercise

Create a second example for Visualization Principles. Change one important condition or input, predict how the result should change, and explain why. Then identify one limitation or common mistake a beginner should watch for.

14.2 General Plotting Tools

General Plotting Tools (a practical concept used within data collection, understanding, cleaning, and preparation). Within Chapter 14, this topic connects directly to data collection, understanding, cleaning, and preparation. The important goal is to understand what information goes into the method, what transformation or decision happens, and what output should be checked.

In a real project, this step can affect every model that comes later. Check data types, missing values, scale, categories, unusual records, and whether the transformation can be repeated consistently on new data. Good preparation reduces avoidable errors and helps make evaluation more trustworthy.

Example

Imagine a small real-world project where General Plotting Tools is the main idea. Identify the input information, the decision or transformation that occurs, and the result you would inspect to decide whether the method is working correctly.

Coding example

// General Plotting Tools
const records = [3, 5, 7, 9, 11];
const transform = value => ({ input: value, output: value * 2 + 1 });
const results = records.map(transform);

console.log(results);

Code explanation

The sample starts with a small list of inputs so every result can be checked manually.
`transform()` represents the main operation for this topic in a deliberately simple form.
`map()` applies the same rule consistently to every item and returns a new result array.
Use this pattern to focus on input, transformation, and output before replacing the toy rule with a more advanced method.

Expected result: A transformed result is printed for each input value.

Practice exercise

Create a second example for General Plotting Tools. Change one important condition or input, predict how the result should change, and explain why. Then identify one limitation or common mistake a beginner should watch for.

14.3 Line Charts

Line Charts (a practical concept used within data collection, understanding, cleaning, and preparation). Within Chapter 14, this topic connects directly to data collection, understanding, cleaning, and preparation. The important goal is to understand what information goes into the method, what transformation or decision happens, and what output should be checked.

In a real project, this step can affect every model that comes later. Check data types, missing values, scale, categories, unusual records, and whether the transformation can be repeated consistently on new data. Good preparation reduces avoidable errors and helps make evaluation more trustworthy.

Example

Imagine a small real-world project where Line Charts is the main idea. Identify the input information, the decision or transformation that occurs, and the result you would inspect to decide whether the method is working correctly.

Coding example

// Line Charts
const records = [3, 5, 7, 9, 11];
const transform = value => ({ input: value, output: value * 2 + 1 });
const results = records.map(transform);

console.log(results);

Code explanation

The sample starts with a small list of inputs so every result can be checked manually.
`transform()` represents the main operation for this topic in a deliberately simple form.
`map()` applies the same rule consistently to every item and returns a new result array.
Use this pattern to focus on input, transformation, and output before replacing the toy rule with a more advanced method.

Expected result: A transformed result is printed for each input value.

Practice exercise

Create a second example for Line Charts. Change one important condition or input, predict how the result should change, and explain why. Then identify one limitation or common mistake a beginner should watch for.

14.4 Bar Charts

Bar Charts (a practical concept used within data collection, understanding, cleaning, and preparation). Within Chapter 14, this topic connects directly to data collection, understanding, cleaning, and preparation. The important goal is to understand what information goes into the method, what transformation or decision happens, and what output should be checked.

In a real project, this step can affect every model that comes later. Check data types, missing values, scale, categories, unusual records, and whether the transformation can be repeated consistently on new data. Good preparation reduces avoidable errors and helps make evaluation more trustworthy.

Example

Imagine a small real-world project where Bar Charts is the main idea. Identify the input information, the decision or transformation that occurs, and the result you would inspect to decide whether the method is working correctly.

Coding example

// Bar Charts
const records = [3, 5, 7, 9, 11];
const transform = value => ({ input: value, output: value * 2 + 1 });
const results = records.map(transform);

console.log(results);

Code explanation

The sample starts with a small list of inputs so every result can be checked manually.
`transform()` represents the main operation for this topic in a deliberately simple form.
`map()` applies the same rule consistently to every item and returns a new result array.
Use this pattern to focus on input, transformation, and output before replacing the toy rule with a more advanced method.

Expected result: A transformed result is printed for each input value.

Practice exercise

Create a second example for Bar Charts. Change one important condition or input, predict how the result should change, and explain why. Then identify one limitation or common mistake a beginner should watch for.

14.5 Histograms

Histograms (a practical concept used within data collection, understanding, cleaning, and preparation). Within Chapter 14, this topic connects directly to data collection, understanding, cleaning, and preparation. The important goal is to understand what information goes into the method, what transformation or decision happens, and what output should be checked.

In a real project, this step can affect every model that comes later. Check data types, missing values, scale, categories, unusual records, and whether the transformation can be repeated consistently on new data. Good preparation reduces avoidable errors and helps make evaluation more trustworthy.

Example

Imagine a small real-world project where Histograms is the main idea. Identify the input information, the decision or transformation that occurs, and the result you would inspect to decide whether the method is working correctly.

Coding example

// Histograms
const records = [3, 5, 7, 9, 11];
const transform = value => ({ input: value, output: value * 2 + 1 });
const results = records.map(transform);

console.log(results);

Code explanation

The sample starts with a small list of inputs so every result can be checked manually.
`transform()` represents the main operation for this topic in a deliberately simple form.
`map()` applies the same rule consistently to every item and returns a new result array.
Use this pattern to focus on input, transformation, and output before replacing the toy rule with a more advanced method.

Expected result: A transformed result is printed for each input value.

Practice exercise

Create a second example for Histograms. Change one important condition or input, predict how the result should change, and explain why. Then identify one limitation or common mistake a beginner should watch for.

14.6 Scatter Plots

Scatter Plots (a practical concept used within data collection, understanding, cleaning, and preparation). Within Chapter 14, this topic connects directly to data collection, understanding, cleaning, and preparation. The important goal is to understand what information goes into the method, what transformation or decision happens, and what output should be checked.

In a real project, this step can affect every model that comes later. Check data types, missing values, scale, categories, unusual records, and whether the transformation can be repeated consistently on new data. Good preparation reduces avoidable errors and helps make evaluation more trustworthy.

Example

Imagine a small real-world project where Scatter Plots is the main idea. Identify the input information, the decision or transformation that occurs, and the result you would inspect to decide whether the method is working correctly.

Coding example

// Scatter Plots
const records = [3, 5, 7, 9, 11];
const transform = value => ({ input: value, output: value * 2 + 1 });
const results = records.map(transform);

console.log(results);

Code explanation

The sample starts with a small list of inputs so every result can be checked manually.
`transform()` represents the main operation for this topic in a deliberately simple form.
`map()` applies the same rule consistently to every item and returns a new result array.
Use this pattern to focus on input, transformation, and output before replacing the toy rule with a more advanced method.

Expected result: A transformed result is printed for each input value.

Practice exercise

Create a second example for Scatter Plots. Change one important condition or input, predict how the result should change, and explain why. Then identify one limitation or common mistake a beginner should watch for.

14.7 Box Plots

Box Plots (a practical concept used within data collection, understanding, cleaning, and preparation). Within Chapter 14, this topic connects directly to data collection, understanding, cleaning, and preparation. The important goal is to understand what information goes into the method, what transformation or decision happens, and what output should be checked.

In a real project, this step can affect every model that comes later. Check data types, missing values, scale, categories, unusual records, and whether the transformation can be repeated consistently on new data. Good preparation reduces avoidable errors and helps make evaluation more trustworthy.

Example

Imagine a small real-world project where Box Plots is the main idea. Identify the input information, the decision or transformation that occurs, and the result you would inspect to decide whether the method is working correctly.

Coding example

// Box Plots
const records = [3, 5, 7, 9, 11];
const transform = value => ({ input: value, output: value * 2 + 1 });
const results = records.map(transform);

console.log(results);

Code explanation

The sample starts with a small list of inputs so every result can be checked manually.
`transform()` represents the main operation for this topic in a deliberately simple form.
`map()` applies the same rule consistently to every item and returns a new result array.
Use this pattern to focus on input, transformation, and output before replacing the toy rule with a more advanced method.

Expected result: A transformed result is printed for each input value.

Practice exercise

Create a second example for Box Plots. Change one important condition or input, predict how the result should change, and explain why. Then identify one limitation or common mistake a beginner should watch for.

14.8 Heatmaps

Heatmaps (a practical concept used within data collection, understanding, cleaning, and preparation). Within Chapter 14, this topic connects directly to data collection, understanding, cleaning, and preparation. The important goal is to understand what information goes into the method, what transformation or decision happens, and what output should be checked.

In a real project, this step can affect every model that comes later. Check data types, missing values, scale, categories, unusual records, and whether the transformation can be repeated consistently on new data. Good preparation reduces avoidable errors and helps make evaluation more trustworthy.

Example

Imagine a small real-world project where Heatmaps is the main idea. Identify the input information, the decision or transformation that occurs, and the result you would inspect to decide whether the method is working correctly.

Coding example

// Heatmaps
const records = [3, 5, 7, 9, 11];
const transform = value => ({ input: value, output: value * 2 + 1 });
const results = records.map(transform);

console.log(results);

Code explanation

The sample starts with a small list of inputs so every result can be checked manually.
`transform()` represents the main operation for this topic in a deliberately simple form.
`map()` applies the same rule consistently to every item and returns a new result array.
Use this pattern to focus on input, transformation, and output before replacing the toy rule with a more advanced method.

Expected result: A transformed result is printed for each input value.

Practice exercise

Create a second example for Heatmaps. Change one important condition or input, predict how the result should change, and explain why. Then identify one limitation or common mistake a beginner should watch for.

14.9 Distribution Plots

Distribution Plots (a practical concept used within data collection, understanding, cleaning, and preparation). Within Chapter 14, this topic connects directly to data collection, understanding, cleaning, and preparation. The important goal is to understand what information goes into the method, what transformation or decision happens, and what output should be checked.

In a real project, this step can affect every model that comes later. Check data types, missing values, scale, categories, unusual records, and whether the transformation can be repeated consistently on new data. Good preparation reduces avoidable errors and helps make evaluation more trustworthy.

Example

Imagine a small real-world project where Distribution Plots is the main idea. Identify the input information, the decision or transformation that occurs, and the result you would inspect to decide whether the method is working correctly.

Coding example

// Distribution Plots
const outcomes = [1, 0, 1, 1, 0, 1, 0, 1];
const successes = outcomes.reduce((sum, x) => sum + x, 0);
const probability = successes / outcomes.length;
const smoothed = (successes + 1) / (outcomes.length + 2);

console.log({ probability: probability.toFixed(3), smoothed: smoothed.toFixed(3) });

Code explanation

Each `1` represents an observed success and each `0` represents a non-success.
Dividing the number of successes by the number of observations gives an empirical probability.
The smoothed estimate adds one pseudo-success and one pseudo-failure so very small datasets are less extreme.
Comparing the raw and smoothed results demonstrates how probabilistic estimates can change when prior information is introduced.

Expected result: Two probability estimates are printed for comparison.

Practice exercise

Create a second example for Distribution Plots. Change one important condition or input, predict how the result should change, and explain why. Then identify one limitation or common mistake a beginner should watch for.

14.10 Multivariate Visualization

Multivariate Visualization (a practical concept used within data collection, understanding, cleaning, and preparation). Within Chapter 14, this topic connects directly to data collection, understanding, cleaning, and preparation. The important goal is to understand what information goes into the method, what transformation or decision happens, and what output should be checked.

In a real project, this step can affect every model that comes later. Check data types, missing values, scale, categories, unusual records, and whether the transformation can be repeated consistently on new data. Good preparation reduces avoidable errors and helps make evaluation more trustworthy.

Example

Imagine a small real-world project where Multivariate Visualization is the main idea. Identify the input information, the decision or transformation that occurs, and the result you would inspect to decide whether the method is working correctly.

Coding example

// Multivariate Visualization
const records = [3, 5, 7, 9, 11];
const transform = value => ({ input: value, output: value * 2 + 1 });
const results = records.map(transform);

console.log(results);

Code explanation

The sample starts with a small list of inputs so every result can be checked manually.
`transform()` represents the main operation for this topic in a deliberately simple form.
`map()` applies the same rule consistently to every item and returns a new result array.
Use this pattern to focus on input, transformation, and output before replacing the toy rule with a more advanced method.

Expected result: A transformed result is printed for each input value.

Practice exercise

Create a second example for Multivariate Visualization. Change one important condition or input, predict how the result should change, and explain why. Then identify one limitation or common mistake a beginner should watch for.

14.11 Interactive Visualization

Interactive Visualization (a practical concept used within data collection, understanding, cleaning, and preparation). Within Chapter 14, this topic connects directly to data collection, understanding, cleaning, and preparation. The important goal is to understand what information goes into the method, what transformation or decision happens, and what output should be checked.

In a real project, this step can affect every model that comes later. Check data types, missing values, scale, categories, unusual records, and whether the transformation can be repeated consistently on new data. Good preparation reduces avoidable errors and helps make evaluation more trustworthy.

Example

Imagine a small real-world project where Interactive Visualization is the main idea. Identify the input information, the decision or transformation that occurs, and the result you would inspect to decide whether the method is working correctly.

Coding example

// Interactive Visualization
const records = [3, 5, 7, 9, 11];
const transform = value => ({ input: value, output: value * 2 + 1 });
const results = records.map(transform);

console.log(results);

Code explanation

The sample starts with a small list of inputs so every result can be checked manually.
`transform()` represents the main operation for this topic in a deliberately simple form.
`map()` applies the same rule consistently to every item and returns a new result array.
Use this pattern to focus on input, transformation, and output before replacing the toy rule with a more advanced method.

Expected result: A transformed result is printed for each input value.

Practice exercise

Create a second example for Interactive Visualization. Change one important condition or input, predict how the result should change, and explain why. Then identify one limitation or common mistake a beginner should watch for.

14.12 Choosing the Correct Chart

Choosing the Correct Chart (a practical concept used within data collection, understanding, cleaning, and preparation). Within Chapter 14, this topic connects directly to data collection, understanding, cleaning, and preparation. The important goal is to understand what information goes into the method, what transformation or decision happens, and what output should be checked.

In a real project, this step can affect every model that comes later. Check data types, missing values, scale, categories, unusual records, and whether the transformation can be repeated consistently on new data. Good preparation reduces avoidable errors and helps make evaluation more trustworthy.

Example

Imagine a small real-world project where Choosing the Correct Chart is the main idea. Identify the input information, the decision or transformation that occurs, and the result you would inspect to decide whether the method is working correctly.

Coding example

// Choosing the Correct Chart
const records = [3, 5, 7, 9, 11];
const transform = value => ({ input: value, output: value * 2 + 1 });
const results = records.map(transform);

console.log(results);

Code explanation

The sample starts with a small list of inputs so every result can be checked manually.
`transform()` represents the main operation for this topic in a deliberately simple form.
`map()` applies the same rule consistently to every item and returns a new result array.
Use this pattern to focus on input, transformation, and output before replacing the toy rule with a more advanced method.

Expected result: A transformed result is printed for each input value.

Practice exercise

Create a second example for Choosing the Correct Chart. Change one important condition or input, predict how the result should change, and explain why. Then identify one limitation or common mistake a beginner should watch for.

Chapter 14 Review Questions and Answers

Q1. What is Visualization Principles?

Answer: Visualization Principles is a practical concept used within data collection, understanding, cleaning, and preparation. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q2. What is General Plotting Tools?

Answer: General Plotting Tools is a practical concept used within data collection, understanding, cleaning, and preparation. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q3. What is Line Charts?

Answer: Line Charts is a practical concept used within data collection, understanding, cleaning, and preparation. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q4. What is Bar Charts?

Answer: Bar Charts is a practical concept used within data collection, understanding, cleaning, and preparation. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q5. What is Histograms?

Answer: Histograms is a practical concept used within data collection, understanding, cleaning, and preparation. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q6. What is Scatter Plots?

Answer: Scatter Plots is a practical concept used within data collection, understanding, cleaning, and preparation. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q7. What is Box Plots?

Answer: Box Plots is a practical concept used within data collection, understanding, cleaning, and preparation. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q8. What is Heatmaps?

Answer: Heatmaps is a practical concept used within data collection, understanding, cleaning, and preparation. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q9. What is Distribution Plots?

Answer: Distribution Plots is a practical concept used within data collection, understanding, cleaning, and preparation. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q10. What is Multivariate Visualization?

Answer: Multivariate Visualization is a practical concept used within data collection, understanding, cleaning, and preparation. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q11. What is Interactive Visualization?

Answer: Interactive Visualization is a practical concept used within data collection, understanding, cleaning, and preparation. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q12. What is Choosing the Correct Chart?

Answer: Choosing the Correct Chart is a practical concept used within data collection, understanding, cleaning, and preparation. In this chapter, focus on the input, the method or decision, and the result that should be checked.