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Machine Learning Algorithms
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Random Forest
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List of algorithms
REGRESSION
Linear Regression
Ordinary Least Squares
Logistic Regression
Stepwise Regression
Multivariate Adaptive Regression Splines
Locally Estimated Scatterplot Smoothing
INSTANCE BASED METHODS
kNearest Neighbour (kNN)
Learning Vector Quantization (LVQ)
SelfOrganizing Map (SOM)
PREPROCESSING DATA
standardization  mean removal  variance scaling
normalization
binarization
encoding categorical variables
label preprocessing
imputation of missing values
unsupervised data reduction
pca
random projections
Feature agglometration
DECISION TREE LEARNING
Decision tree
Random Forest
Gradient Boosting Machines (GBM)
BAYESIAN
Naive Bayes
Averaged OneDependence Estimators
Bayesian Belief Network (BBN)
COLLABORATIVE FILTERING
userbased collaborative filtering
itembased collaborative filtering
matrix factorization with ALS
CLUSTERING
kmeans
fuzzy kmeans
affinity propagation
mean shift
spectral clustering
hierarchical clustering
Agglomerative hierarchical clustering
Divisive hierarchical clustering
DIMENSIONALITY REDUCTION
pca
Singular Value Decomposition svd
Lanczos Algorithm
KERNEL METHODS
Support Vector Machines (SVM)
Radial Basis Function (RBF)
Linear Discriminant Analysis (LDA)
ENSEMBLE METHODS
Bagging
Boosting
Bootstrapped Aggregation
AdaBoost
Stacked Generalization
Gradient Boosting Machines (GBM)
Random Forest
Mixture of Experts
ASSOCIATION LEARNING
apriori
eclat
fp growth
NATURAL LANGUAGE PROCESSING
lda
OTHER CONCEPTS
anova
max entropy
pattern
Posts
Random forests  classification description
Random Forests Leo Breiman and Adele Cutler
06Jan2015
How Random Forest algorithm works
In this video I explain very briefly how the Random Forest algorithm works with a simple example composed by 4 decision trees.
08Sep2014
08Apr2014
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