Index
A
B
C
D
E
F
G
- GBM, with LAD
- generalized linear model (GLM)
- get-pi.py script
- Google Books
- Gradient Boosting Regressor
- Gradient Descend
- Gradient Descent
- gradient descent
- Greedy feature selection / Greedy selection of features
- grid search
H
I
J
K
L
- label ranking average precision (LRAP) / A ranking problem
- LARS algorithm
- lasso (L1 regularization)
- learning rate
- Least Absolute Deviations (LAD)
- Least Angle Regression (LARS)
- linear model family
- linear models
- linear regression
- linear transformation
- logistic function
- logistic regression
- logit function
M
- Madelon dataset
- Markdown language
- Mean Absolute Error (MAE)
- median
- mini-batch learning / Online mini-batch learning
- MinMaxScaler class / Mean centering
- missing data
- mode
- model evaluation
- Multiclass Logistic Regression
- multiple regression
N
- natural language processing (NLP)
- no free lunch
/ The challenge
- normal distribution
- normalization
- Not a Number (NaN)
- numeric feature
- NumPy
O
P
- Pandas
- Pandas DataFrame
/ Starting from the basics
- partial residual plot
- Patsy package
- pip
- polynomial regression
- precision score
/ Assessing the classifier's performance
- prediction
- predictive variables
- Principal Component Analysis (PCA)
- probability-based approach
- probability density function (PDF)
- Pseudoinverse
- Python
- Python(x,y)
- Python 2
- Python 3
- Python packages
Q
- QR factorization
- quadratic transformation
- qualitative feature
R
- R
- R-squared
- random grid search
- ranking problem
- recursive feature selection
- regression analysis
- regression problem
- regularization
- reinforcement learning
- residuals
- residuals, linear regression
- response variables
- reversals
- ridge (L2 regularization)
- root mean squared error
/ Greedy selection of features
S
T
U
V
W
Z
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