Main Filters
Main Filters
I started to compare the functionalities of some Python extensions (the list is not exhaustive) :
A couple of forums, kind of FAQ for machine learning:
It would be difficult to do machine learning without using visualization tools. matplotlib and ggplot would be a good way to start. We also manipulate tables: numpy and pandas. For a command line: ipython or bpython are two common options.
If you are looking for data UC Irvine Machine Learning Repository. If you work with Windows, many of the presented modules can be downloaded from Unofficial Windows Binaries for Python Extension Packages. It also gives a clear view of what package is available on which Python's version.
Next table summarizes where you can find which features (with some errors):
scikit-learn | statsmodels | mlpy | MDP | PyBrain | Theano | MILK | pyMVPA | NLTK | Gensim | Orange | |
AdaBoost | yes | yes | |||||||||
ANOVA | yes | yes | |||||||||
ARMA (Time Series) | yes | ||||||||||
C4.5 | yes | yes | |||||||||
Canonical Correlation Analysis | yes | yes | |||||||||
Cross Validation | yes | yes | |||||||||
DBSCAN | yes | ||||||||||
Decision Trees | yes | yes | yes | yes | |||||||
Deep Belief Networks | yes | ||||||||||
Dictionary Learning | yes | ||||||||||
Dynamic Time Warping (yes) | yes | ||||||||||
Elastic Net | yes | yes | yes | yes | |||||||
Evolution Strategies (ES) | yes | ||||||||||
Fast ICA | yes | yes | |||||||||
Fast/Partial PCA | yes | ||||||||||
Features Selection | yes | yes | yes | yes | |||||||
Gaussian Mixture Model | yes | yes | |||||||||
Gaussian Naive Bayes | yes | yes | |||||||||
Genetic Algorithm | yes | ||||||||||
Golub Classifier | yes | ||||||||||
GPU computation | yes | ||||||||||
Gradient Based Optimization | yes | yes | |||||||||
Gradient Boosted Tree | yes | ||||||||||
Gradient Boosting Regression | yes | yes | |||||||||
Grid Search | yes | ||||||||||
Hidden Markov Model with Gaussian Mixture Emissions (HMM GMM) | yes | yes | |||||||||
Hierarchical Clustering (Ward…) | yes | yes | yes | yes | |||||||
Hierarchical Dirichlet Application (HDP) | yes | ||||||||||
ICA | yes | yes | |||||||||
Isotonic Regression | yes | ||||||||||
KDTree | yes | ||||||||||
Kernal Density | yes | yes | |||||||||
Kernel Fisher Discriminant | yes | ||||||||||
Kernel PCA | yes | yes | yes | ||||||||
Kernel Regression | yes | ||||||||||
Kernel Ridge Regression | yes | ||||||||||
k-Means | yes | yes | yes | yes | yes | ||||||
k-NN | yes | yes | yes | yes | yes | ||||||
Label Spreading | yes | yes | |||||||||
Largest Common Subsequence (LCS) | |||||||||||
Lasso | yes | yes | |||||||||
Large Linear Classification | yes | ||||||||||
Latent Dirichlet Application (LDA) | yes | ||||||||||
Least Angle Regression (LARS) | yes | yes | yes | ||||||||
Linear Discriminant Analysis (LDA) | yes | yes | yes | yes | |||||||
Linear Regression | yes | yes | yes | yes | yes | yes | yes | ||||
Logisitic Regression | yes | yes | yes | yes | yes | ||||||
Naive Bayesian Learner | yes | yes | |||||||||
Natural Language Processing (NLP) | yes | ||||||||||
Neural Network (NN) | yes | yes | yes | yes | |||||||
Non-Negative matrix factorization by Projected Gradient (NMF) | yes | yes | |||||||||
Partial Least Square (PLS) | yes | yes | |||||||||
Partial Least Square (SVD) | yes | ||||||||||
Particle Swarm Optimization (PSO) | yes | ||||||||||
Passive Aggressive Classification | yes | ||||||||||
Passive Aggressive Regression | yes | ||||||||||
Pipeline | yes | yes | |||||||||
Principal Component Analysis (PCA) | yes | yes | yes | yes | yes | ||||||
Probabilistic Principal Component Analysis (pPCA) | yes | yes | yes | ||||||||
p-Value | yes | yes | |||||||||
Quadratic Discriminant Analysis (QDA) | yes | yes | |||||||||
Random Forests | yes | yes | yes | yes | |||||||
Recurrent Neural Network | yes | ||||||||||
Regression Tree | yes | yes | yes | ||||||||
Reinforcement Learning | yes | ||||||||||
Ridge Regression | yes | yes | yes | yes | |||||||
ROC / Precision / Recall | yes | yes | |||||||||
SARSA | yes | ||||||||||
Self Organizing Map (SOM - Kohonen) | yes | yes | yes | ||||||||
Singular Value Decomposition (SVD) | yes | yes | |||||||||
Sparse PCA | yes | yes | |||||||||
Spectral BiClustering | yes | ||||||||||
Spectral Clustering | yes | ||||||||||
Spectral Coclustering | yes | ||||||||||
Spectral Regression Discriminant Analysis | yes | ||||||||||
Support Vector Classificiation (SVC) | yes | yes | yes | ||||||||
Support Vector Machine (SVM) | yes | yes | yes | yes | yes | yes | yes | ||||
Support Vector Regression (SVR) | yes | yes | |||||||||
TF-IDF | yes | yes | |||||||||
Wavelets | yes | yes |
If the model you need is not in the previous list, you can use rpy2 to communicate with R where you will surely find a related package.
2014/09/03: you can also read Python Tools for Machine Learning.
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