Thinking-data-science-a-data-science-practitioners-guide

xiv
Contents
XGBoost . . . . . . . . . . ................................... 120
Implementation . . . . . . . . . . . . . . . . . . . ................... 121
XGBRegressor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
XGBClassifier....................................... 122
CatBoost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
Implementation . . . . . . . . . . . . . . . . . . . ................... 123
CatBoostRegressor .................................... 123
CatBoostClassifier.................................... 124
LightGBM ............................................ 125
Implementation . . . . . . . . . . . . . . . . . . . ................... 126
The LGBMRegressor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
The LGBMClassifier................................... 127
Performance Summary ................................... 128
Summary ............................................. 129
6 K-Nearest Neighbors .................................... 131
In a Nutshell ........................................... 131
K-Nearest Neighbors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
KNN Algorithm . . . . .................................... 132
KNN Working . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Effect of K ............................................ 135
Advantages . ........................................... 135
Disadvantages of KNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
Implementation . . . . . . . . . . . . . . . . . . . ................... 136
Project . . ............................................. 137
Loading Dataset ...................................... 137
Determining K Optimal ................................. 138
Model Training ...................................... 140
Model Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
When to Use? . . ........................................ 141
Summary ............................................. 141
7 Naive Bayes ........................................... 143
In a Nutshell ........................................... 143
When to Use? . . ........................................ 144
Naive Bayes Theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
Applying the Theorem . ................................ 145
Advantages ......................................... 146
Disadvantages ....................................... 147
Improving Performance ................................. 147
Naive Bayes Types . ..................................... 148
Multinomial Naive Bayes ............................... 148
Bernoulli Naive Bayes ................................. 148
Gaussian Naive Bayes .................................. 148
Complement Naive Bayes . .............................. 149
Categorical Naive Bayes . . .............................. 149