Index
A
- absolute deviation method
- accuracy
- AdaBoost
- anonymous functions
- arange
- arrays
- axioms
B
- Bagging
- BaseEstimator
- Boosting
- Bootstrapping
- box-and-whisker plot
- built-in morphy-function
C
- classification
- classification model
- columns
- comprehension
- confusion matrix
- cost function
- counter
- CountVectorizer class
- cross-validation iterators
- csv library, Python
- curse of dimensionality
D
- data
- grouping / Grouping the data and using dot plots, How to do it…, How it works…
- imputing / Imputing the data, Getting ready, How it works…
- scaling / Getting ready, How it works…, There's more…
- standardizing / Getting ready, How it works…, There's more…
- preparing, for classification model / Preparing data for model building, How to do it…, How it works…, There's more...
- data clustering
- data dimension
- data model, Python
- data preprocessing
- dataset
- decision trees
- decorators
- deque
- derivational patterns
- dictionaries, Python
- dictionary objects
- dictionary of dictionaries
- dimensionality reduction
- distance measures
- documents
- dot plots
E
- Ensemble methods
- error rate
- Exploratory Data Analysis (EDA)
- ExtraTreesClassifier class
- Extremely Randomized Trees
F
- feature matrices
- feature test condition
- filters
- function
- functools
G
- generator
- Gradient Boosting
- Graphviz package
H
I
- information gain
- instance-based learning
- inverse document frequencies
- iterables
- iterator
- itertools
- Itertools.dropwhile
- izip
K
- k-fold cross-validation
- k-means method
- K-Nearest Neighbor (KNN)
- kernel-based perceptron
- kernel methods
- kernel PCA
- key
L
- L1 shrinkage
- L1 shrinkage (LASSO)
- L2 shrinkage
- L2 shrinkage (ridge)
- lambda
- Last In, First Out (LIFO)
- Latent Semantic Analysis (LSA)
- lazy learner
- Least absolute shrinkage and selection operator (LASSO)
- least squares
- lemmatization
- linear kernel
- linear kernel;about / There's more...
- list
- list comprehension
- loadtxt method, NumPy
- Local Outlier Factor (LOF)
M
- machine learning
- map function
- matplotlib
- matrix decomposition
- multiclass problems
- multivariate data
N
- namedtuple
- Naïve Bayes
- nearest neighbors
- Non-negative Matrix Factorization (NMF)
- NumPy
- NumPy libraries
O
- online learning algorithm
- OrderedDict container
- out-of-bag estimation (OOB)
- outliers
P
- pairwise submodule
- partial_fit method
- percentiles, NumPy
- perceptron
- polynomial
- polynomial kernel
- polysemy
- Principal Component Analysis (PCA)
- principal components
- priority queues
- progressive sampling
- pyplot
- Python heapq documentation
- Python libraries
- Python NLTK library
R
- Radial Basis Function (RBF) kernel
- Random Forest
- RandomForestClassifier class
- randomization
- random projection
- random sampling
- real-valued numbers
- recursive feature selection
- regression
- about / Predicting real-valued numbers using regression
- used, for predicting real-valued numbers / Predicting real-valued numbers using regression, How to do it…, How it works…, There's more...
- with L2 shrinkage (ridge) / Learning regression with L2 shrinkage – ridge, How to do it…, How it works…, There's more…
- with L1 shrinkage (LASSO) / Learning regression with L1 shrinkage – LASSO, How to do it…, How it works…
- stochastic gradient descent, using / Using stochastic gradient descent for regression, How to do it…, How it works…, There's more…
- ridge regression
- Rotational Forest
- rote classifier algorithm
S
- scatter plots
- scikit-learn
- SciPy
- sent_tokenize function
- set builder notation
- sets
- SGD regressor
- shrinkage
- Singular Value Decomposition (SVD)
- snowball stemmers
- sparse matrix representation
- standardization
- star convex-shaped data points
- stemming, words
- stochastic gradient descent
- stop words
- stratified sampling
- summary statistics
T
- tabular data
- term frequencies
- Term Frequency Inverse Document Frequency (TFIDF)
- text
- text mining
- TFIDF transformer
- tokenization
- tuples
U
V
W
- word lemmatization
- words
- word_tokenize function
Z
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