Machine Learning Chapter 15: Feature Engineering

What this chapter covers

This chapter contains 15 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.

15.1 What Is a Feature?

What Is a Feature? (an input value or measurable property given to a model). Within Chapter 15, 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

For predicting a house price, floor area, number of bedrooms, and neighbourhood can be input features. The sale price is the target value the model tries to predict.

Coding example

// What Is a Feature?
const rows = [
  { age: 22, score: 71 },
  { age: null, score: 88 },
  { age: 35, score: 93 }
];

const knownAges = rows.filter(r => r.age !== null).map(r => r.age);
const fallbackAge = knownAges.reduce((a,b) => a+b, 0) / knownAges.length;
const cleaned = rows.map(r => ({ ...r, age: r.age ?? fallbackAge }));

console.log(cleaned);

Code explanation

The sample rows deliberately contain one missing value so you can see a preprocessing decision.
Known ages are separated and averaged to create a simple fallback value.
`map()` builds a new cleaned dataset instead of modifying the original rows in place.
The final log lets you verify that every row now has a usable numeric age.

Expected result: A cleaned array is printed with the missing age filled.

Practice exercise

Create a second example for What Is a Feature?. 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.

15.2 Feature Selection

Feature Selection (an input value or measurable property given to a model). Within Chapter 15, 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

For predicting a house price, floor area, number of bedrooms, and neighbourhood can be input features. The sale price is the target value the model tries to predict.

Coding example

// Feature Selection
const rows = [
  { age: 22, score: 71 },
  { age: null, score: 88 },
  { age: 35, score: 93 }
];

const knownAges = rows.filter(r => r.age !== null).map(r => r.age);
const fallbackAge = knownAges.reduce((a,b) => a+b, 0) / knownAges.length;
const cleaned = rows.map(r => ({ ...r, age: r.age ?? fallbackAge }));

console.log(cleaned);

Code explanation

The sample rows deliberately contain one missing value so you can see a preprocessing decision.
Known ages are separated and averaged to create a simple fallback value.
`map()` builds a new cleaned dataset instead of modifying the original rows in place.
The final log lets you verify that every row now has a usable numeric age.

Expected result: A cleaned array is printed with the missing age filled.

Practice exercise

Create a second example for Feature Selection. 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.

15.3 Feature Extraction

Feature Extraction (an input value or measurable property given to a model). Within Chapter 15, 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

For predicting a house price, floor area, number of bedrooms, and neighbourhood can be input features. The sale price is the target value the model tries to predict.

Coding example

// Feature Extraction
const rows = [
  { age: 22, score: 71 },
  { age: null, score: 88 },
  { age: 35, score: 93 }
];

const knownAges = rows.filter(r => r.age !== null).map(r => r.age);
const fallbackAge = knownAges.reduce((a,b) => a+b, 0) / knownAges.length;
const cleaned = rows.map(r => ({ ...r, age: r.age ?? fallbackAge }));

console.log(cleaned);

Code explanation

The sample rows deliberately contain one missing value so you can see a preprocessing decision.
Known ages are separated and averaged to create a simple fallback value.
`map()` builds a new cleaned dataset instead of modifying the original rows in place.
The final log lets you verify that every row now has a usable numeric age.

Expected result: A cleaned array is printed with the missing age filled.

Practice exercise

Create a second example for Feature Extraction. 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.

15.4 Numerical Features

Numerical Features (an input value or measurable property given to a model). Within Chapter 15, 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

For predicting a house price, floor area, number of bedrooms, and neighbourhood can be input features. The sale price is the target value the model tries to predict.

Coding example

// Numerical Features
const rows = [
  { age: 22, score: 71 },
  { age: null, score: 88 },
  { age: 35, score: 93 }
];

const knownAges = rows.filter(r => r.age !== null).map(r => r.age);
const fallbackAge = knownAges.reduce((a,b) => a+b, 0) / knownAges.length;
const cleaned = rows.map(r => ({ ...r, age: r.age ?? fallbackAge }));

console.log(cleaned);

Code explanation

The sample rows deliberately contain one missing value so you can see a preprocessing decision.
Known ages are separated and averaged to create a simple fallback value.
`map()` builds a new cleaned dataset instead of modifying the original rows in place.
The final log lets you verify that every row now has a usable numeric age.

Expected result: A cleaned array is printed with the missing age filled.

Practice exercise

Create a second example for Numerical Features. 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.

15.5 Categorical Features

Categorical Features (an input value or measurable property given to a model). Within Chapter 15, 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

For predicting a house price, floor area, number of bedrooms, and neighbourhood can be input features. The sale price is the target value the model tries to predict.

Coding example

// Categorical Features
const rows = [
  { age: 22, score: 71 },
  { age: null, score: 88 },
  { age: 35, score: 93 }
];

const knownAges = rows.filter(r => r.age !== null).map(r => r.age);
const fallbackAge = knownAges.reduce((a,b) => a+b, 0) / knownAges.length;
const cleaned = rows.map(r => ({ ...r, age: r.age ?? fallbackAge }));

console.log(cleaned);

Code explanation

The sample rows deliberately contain one missing value so you can see a preprocessing decision.
Known ages are separated and averaged to create a simple fallback value.
`map()` builds a new cleaned dataset instead of modifying the original rows in place.
The final log lets you verify that every row now has a usable numeric age.

Expected result: A cleaned array is printed with the missing age filled.

Practice exercise

Create a second example for Categorical Features. 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.

15.6 Binary Features

Binary Features (an input value or measurable property given to a model). Within Chapter 15, 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

For predicting a house price, floor area, number of bedrooms, and neighbourhood can be input features. The sale price is the target value the model tries to predict.

Coding example

// Binary Features
const rows = [
  { age: 22, score: 71 },
  { age: null, score: 88 },
  { age: 35, score: 93 }
];

const knownAges = rows.filter(r => r.age !== null).map(r => r.age);
const fallbackAge = knownAges.reduce((a,b) => a+b, 0) / knownAges.length;
const cleaned = rows.map(r => ({ ...r, age: r.age ?? fallbackAge }));

console.log(cleaned);

Code explanation

The sample rows deliberately contain one missing value so you can see a preprocessing decision.
Known ages are separated and averaged to create a simple fallback value.
`map()` builds a new cleaned dataset instead of modifying the original rows in place.
The final log lets you verify that every row now has a usable numeric age.

Expected result: A cleaned array is printed with the missing age filled.

Practice exercise

Create a second example for Binary Features. 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.

15.7 Date and Time Features

Date and Time Features (an input value or measurable property given to a model). Within Chapter 15, 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

For predicting a house price, floor area, number of bedrooms, and neighbourhood can be input features. The sale price is the target value the model tries to predict.

Coding example

// Date and Time Features
const rows = [
  { age: 22, score: 71 },
  { age: null, score: 88 },
  { age: 35, score: 93 }
];

const knownAges = rows.filter(r => r.age !== null).map(r => r.age);
const fallbackAge = knownAges.reduce((a,b) => a+b, 0) / knownAges.length;
const cleaned = rows.map(r => ({ ...r, age: r.age ?? fallbackAge }));

console.log(cleaned);

Code explanation

The sample rows deliberately contain one missing value so you can see a preprocessing decision.
Known ages are separated and averaged to create a simple fallback value.
`map()` builds a new cleaned dataset instead of modifying the original rows in place.
The final log lets you verify that every row now has a usable numeric age.

Expected result: A cleaned array is printed with the missing age filled.

Practice exercise

Create a second example for Date and Time Features. 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.

15.8 Text Features

Text Features (an input value or measurable property given to a model). Within Chapter 15, 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

For predicting a house price, floor area, number of bedrooms, and neighbourhood can be input features. The sale price is the target value the model tries to predict.

Coding example

// Text Features
const rows = [
  { age: 22, score: 71 },
  { age: null, score: 88 },
  { age: 35, score: 93 }
];

const knownAges = rows.filter(r => r.age !== null).map(r => r.age);
const fallbackAge = knownAges.reduce((a,b) => a+b, 0) / knownAges.length;
const cleaned = rows.map(r => ({ ...r, age: r.age ?? fallbackAge }));

console.log(cleaned);

Code explanation

The sample rows deliberately contain one missing value so you can see a preprocessing decision.
Known ages are separated and averaged to create a simple fallback value.
`map()` builds a new cleaned dataset instead of modifying the original rows in place.
The final log lets you verify that every row now has a usable numeric age.

Expected result: A cleaned array is printed with the missing age filled.

Practice exercise

Create a second example for Text Features. 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.

15.9 Interaction Features

Interaction Features (an input value or measurable property given to a model). Within Chapter 15, 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

For predicting a house price, floor area, number of bedrooms, and neighbourhood can be input features. The sale price is the target value the model tries to predict.

Coding example

// Interaction Features
const rows = [
  { age: 22, score: 71 },
  { age: null, score: 88 },
  { age: 35, score: 93 }
];

const knownAges = rows.filter(r => r.age !== null).map(r => r.age);
const fallbackAge = knownAges.reduce((a,b) => a+b, 0) / knownAges.length;
const cleaned = rows.map(r => ({ ...r, age: r.age ?? fallbackAge }));

console.log(cleaned);

Code explanation

The sample rows deliberately contain one missing value so you can see a preprocessing decision.
Known ages are separated and averaged to create a simple fallback value.
`map()` builds a new cleaned dataset instead of modifying the original rows in place.
The final log lets you verify that every row now has a usable numeric age.

Expected result: A cleaned array is printed with the missing age filled.

Practice exercise

Create a second example for Interaction Features. 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.

15.10 Polynomial Features

Polynomial Features (an input value or measurable property given to a model). Within Chapter 15, 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

For predicting a house price, floor area, number of bedrooms, and neighbourhood can be input features. The sale price is the target value the model tries to predict.

Coding example

// Polynomial Features
const rows = [
  { age: 22, score: 71 },
  { age: null, score: 88 },
  { age: 35, score: 93 }
];

const knownAges = rows.filter(r => r.age !== null).map(r => r.age);
const fallbackAge = knownAges.reduce((a,b) => a+b, 0) / knownAges.length;
const cleaned = rows.map(r => ({ ...r, age: r.age ?? fallbackAge }));

console.log(cleaned);

Code explanation

The sample rows deliberately contain one missing value so you can see a preprocessing decision.
Known ages are separated and averaged to create a simple fallback value.
`map()` builds a new cleaned dataset instead of modifying the original rows in place.
The final log lets you verify that every row now has a usable numeric age.

Expected result: A cleaned array is printed with the missing age filled.

Practice exercise

Create a second example for Polynomial Features. 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.

15.11 Domain-Based Features

Domain-Based Features (an input value or measurable property given to a model). Within Chapter 15, 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

For predicting a house price, floor area, number of bedrooms, and neighbourhood can be input features. The sale price is the target value the model tries to predict.

Coding example

// Domain-Based Features
const rows = [
  { age: 22, score: 71 },
  { age: null, score: 88 },
  { age: 35, score: 93 }
];

const knownAges = rows.filter(r => r.age !== null).map(r => r.age);
const fallbackAge = knownAges.reduce((a,b) => a+b, 0) / knownAges.length;
const cleaned = rows.map(r => ({ ...r, age: r.age ?? fallbackAge }));

console.log(cleaned);

Code explanation

The sample rows deliberately contain one missing value so you can see a preprocessing decision.
Known ages are separated and averaged to create a simple fallback value.
`map()` builds a new cleaned dataset instead of modifying the original rows in place.
The final log lets you verify that every row now has a usable numeric age.

Expected result: A cleaned array is printed with the missing age filled.

Practice exercise

Create a second example for Domain-Based Features. 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.

15.12 Feature Transformation

Feature Transformation (an input value or measurable property given to a model). Within Chapter 15, 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

For predicting a house price, floor area, number of bedrooms, and neighbourhood can be input features. The sale price is the target value the model tries to predict.

Coding example

// Feature Transformation
const rows = [
  { age: 22, score: 71 },
  { age: null, score: 88 },
  { age: 35, score: 93 }
];

const knownAges = rows.filter(r => r.age !== null).map(r => r.age);
const fallbackAge = knownAges.reduce((a,b) => a+b, 0) / knownAges.length;
const cleaned = rows.map(r => ({ ...r, age: r.age ?? fallbackAge }));

console.log(cleaned);

Code explanation

The sample rows deliberately contain one missing value so you can see a preprocessing decision.
Known ages are separated and averaged to create a simple fallback value.
`map()` builds a new cleaned dataset instead of modifying the original rows in place.
The final log lets you verify that every row now has a usable numeric age.

Expected result: A cleaned array is printed with the missing age filled.

Practice exercise

Create a second example for Feature Transformation. 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.

15.13 Feature Importance

Feature Importance (an input value or measurable property given to a model). Within Chapter 15, 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

For predicting a house price, floor area, number of bedrooms, and neighbourhood can be input features. The sale price is the target value the model tries to predict.

Coding example

// Feature Importance
const rows = [
  { age: 22, score: 71 },
  { age: null, score: 88 },
  { age: 35, score: 93 }
];

const knownAges = rows.filter(r => r.age !== null).map(r => r.age);
const fallbackAge = knownAges.reduce((a,b) => a+b, 0) / knownAges.length;
const cleaned = rows.map(r => ({ ...r, age: r.age ?? fallbackAge }));

console.log(cleaned);

Code explanation

The sample rows deliberately contain one missing value so you can see a preprocessing decision.
Known ages are separated and averaged to create a simple fallback value.
`map()` builds a new cleaned dataset instead of modifying the original rows in place.
The final log lets you verify that every row now has a usable numeric age.

Expected result: A cleaned array is printed with the missing age filled.

Practice exercise

Create a second example for Feature Importance. 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.

15.14 Automated Feature Engineering

Automated Feature Engineering (creating, selecting, or transforming inputs so a model can learn useful patterns). Within Chapter 15, 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

For predicting a house price, floor area, number of bedrooms, and neighbourhood can be input features. The sale price is the target value the model tries to predict.

Coding example

// Automated Feature Engineering
const rows = [
  { age: 22, score: 71 },
  { age: null, score: 88 },
  { age: 35, score: 93 }
];

const knownAges = rows.filter(r => r.age !== null).map(r => r.age);
const fallbackAge = knownAges.reduce((a,b) => a+b, 0) / knownAges.length;
const cleaned = rows.map(r => ({ ...r, age: r.age ?? fallbackAge }));

console.log(cleaned);

Code explanation

The sample rows deliberately contain one missing value so you can see a preprocessing decision.
Known ages are separated and averaged to create a simple fallback value.
`map()` builds a new cleaned dataset instead of modifying the original rows in place.
The final log lets you verify that every row now has a usable numeric age.

Expected result: A cleaned array is printed with the missing age filled.

Practice exercise

Create a second example for Automated Feature Engineering. 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.

15.15 Feature Stores

Feature Stores (an input value or measurable property given to a model). Within Chapter 15, 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

For predicting a house price, floor area, number of bedrooms, and neighbourhood can be input features. The sale price is the target value the model tries to predict.

Coding example

// Feature Stores
const rows = [
  { age: 22, score: 71 },
  { age: null, score: 88 },
  { age: 35, score: 93 }
];

const knownAges = rows.filter(r => r.age !== null).map(r => r.age);
const fallbackAge = knownAges.reduce((a,b) => a+b, 0) / knownAges.length;
const cleaned = rows.map(r => ({ ...r, age: r.age ?? fallbackAge }));

console.log(cleaned);

Code explanation

The sample rows deliberately contain one missing value so you can see a preprocessing decision.
Known ages are separated and averaged to create a simple fallback value.
`map()` builds a new cleaned dataset instead of modifying the original rows in place.
The final log lets you verify that every row now has a usable numeric age.

Expected result: A cleaned array is printed with the missing age filled.

Practice exercise

Create a second example for Feature Stores. 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 15 Review Questions and Answers

Q1. What is What Is a Feature??

Answer: What Is a Feature? is an input value or measurable property given to a model. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q2. What is Feature Selection?

Answer: Feature Selection is an input value or measurable property given to a model. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q3. What is Feature Extraction?

Answer: Feature Extraction is an input value or measurable property given to a model. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q4. What is Numerical Features?

Answer: Numerical Features is an input value or measurable property given to a model. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q5. What is Categorical Features?

Answer: Categorical Features is an input value or measurable property given to a model. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q6. What is Binary Features?

Answer: Binary Features is an input value or measurable property given to a model. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q7. What is Date and Time Features?

Answer: Date and Time Features is an input value or measurable property given to a model. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q8. What is Text Features?

Answer: Text Features is an input value or measurable property given to a model. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q9. What is Interaction Features?

Answer: Interaction Features is an input value or measurable property given to a model. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q10. What is Polynomial Features?

Answer: Polynomial Features is an input value or measurable property given to a model. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q11. What is Domain-Based Features?

Answer: Domain-Based Features is an input value or measurable property given to a model. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q12. What is Feature Transformation?

Answer: Feature Transformation is an input value or measurable property given to a model. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q13. What is Feature Importance?

Answer: Feature Importance is an input value or measurable property given to a model. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q14. What is Automated Feature Engineering?

Answer: Automated Feature Engineering is creating, selecting, or transforming inputs so a model can learn useful patterns. In this chapter, focus on the input, the method or decision, and the result that should be checked.

Q15. What is Feature Stores?

Answer: Feature Stores is an input value or measurable property given to a model. In this chapter, focus on the input, the method or decision, and the result that should be checked.