Image Data
129
🏗️ Shifting, Flipping, and Rotation
Feature Engineering A-Z
Preface
Introduction
Numeric Features
1
Numeric Overview
2
Logarithms
3
Square Root
4
Box-Cox
5
Yeo-Johnson
6
Percentile Scaling
7
Normalization
8
Range Scaling
9
Max Abs Scaling
10
Robust Scaling
11
Binning
12
Splines
13
Polynomial Expansion
14
Arithmetic
Categorical Features
15
Categorical Overview
16
Cleaning
17
Unseen Levels
18
Dummy Encoding
19
Label Encoding
20
Ordinal Encoding
21
Binary Encoding
22
Frequency Encoding
23
Target Encoding
24
Hashing Encoding
25
Leave One Out Encoding
26
Leaf Encoding
27
GLMM Encoding
28
Catboost Encoding
29
Weight of Evidence Encoding
30
James-Stein Encoding
31
M-Estimator Encoding
32
Thermometer Encoding
33
Quantile Encoding
34
Summary Encoding
35
Collapsing Categories
36
Categorical Combination
37
Multi-Dummy Encoding
Datetime Features
38
Datetime Overview
39
Value Extraction
40
Advanced Features
41
Periodic Features
Missing Data
42
Missing Overview
43
Simple Imputation
44
Model Based Imputation
45
Missing Values Indicators
46
Remove Missing Values
Text Features
47
Text Overview
48
Manual Text Features
49
Text Cleaning
50
Tokenization
51
Stemming
52
N-grams
53
Stop words
54
Token Filter
55
Term Frequency
56
TF-IDF
57
Token Hashing
58
Sequence Encoding
59
LDA
60
word2vec
61
BERT
Periodic Features
62
Periodic Overview
63
Trigonometric
64
Periodic Splines
65
Periodic Indicators
Too Many Variables
66
Too Many Overview
67
Zero Variance Filter
68
Principal Component Analysis
69
Principal Component Analysis Variants
70
Independent Component Analysis
71
Non-Negative Matrix Factorization
72
Partial Least Squares
73
Linear Discriminant Analysis
74
LDA Variants
75
Autoencoders
76
Uniform Manifold Approximation and Projection
77
ISOMAP
78
Filter based feature selection
Correlated Data
79
Correlated Overview
80
High Correlation Filter
Outliers
81
Outliers Overview
82
Identify
83
Outlier Removal
84
Imputation
85
Indicate
Imbalanced Data
86
Imbalanced Overview
87
Up-Sampling
88
SMOTE
89
SMOTE Variants
90
Down-Sampling
91
Near-Miss
92
Tomek Link Removal
93
Condensed Nearest Neighbor
94
Edited Nearest Neighbor
95
Instance Hardness Threshold
Miscellaneous
96
Miscellaneous Overview
97
IDs
98
Colors
99
🏗️ Zip Codes
100
🏗️ Emails
Spatial
101
Spatial Overview
102
🏗️ Spatial Distance
103
🏗️ Spatial Nearest
104
🏗️ Spatial Count
105
🏗️ Spatial Query
106
🏗️ Spatial Embedding
107
🏗️ Spatial Characteristics
Time-Series Data
108
Time-series Overview
109
🏗️ Smoothing
110
🏗️ Sliding
111
🏗️ Log Interval
112
🏗️ Time series Missing values
113
🏗️ Time Series outliers
114
🏗️ Differences
115
🏗️ Lagging Features
116
🏗️ Rolling Window
117
🏗️ Expanding Window
118
🏗️ Fourier Features
119
🏗️ Wavelet
Image Data
120
Image Overview
121
🏗️ Edge and corner detection
122
🏗️ Texture Analysis
123
🏗️ Greyscale conversion
124
🏗️ Color Modifications
125
🏗️ Noise Reduction
126
🏗️ Value Normalization
127
🏗️ Resizing
128
🏗️ Changing Brightness
129
🏗️ Shifting, Flipping, and Rotation
130
🏗️ Cropping and Scaling
131
🏗️ Image embeddings
Ralational Data
132
Relational Overview
133
🏗️ Manual
134
🏗️ Automatic
Video Data
135
Video Overview
136
🏗️ Temporary
Sound Data
137
Sound Overview
138
🏗️ Temporary
139
🏗️ Order of transformations
140
🏗️ What should you do if you have sparse data?
141
🏗️ How Different Models Deal With Input
142
🏗️ Summary
References
Table of contents
129.1
Shifting, Flipping, and Rotation
129.2
Pros and Cons
129.2.1
Pros
129.2.2
Cons
129.3
R Examples
129.4
Python Examples
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Image Data
129
🏗️ Shifting, Flipping, and Rotation
129
🏗️ Shifting, Flipping, and Rotation
129.1
Shifting, Flipping, and Rotation
WIP
129.2
Pros and Cons
129.2.1
Pros
129.2.2
Cons
129.3
R Examples
129.4
Python Examples
128
🏗️ Changing Brightness
130
🏗️ Cropping and Scaling