The document provides an introduction to machine learning using Python's Orange library, covering data loading, exploration, and visualization methods. It details classifiers such as Bayes and decision trees, evaluation techniques like cross-validation, and ensemble methods. Additionally, it touches on feature selection and other algorithms, including supervised, unsupervised, and reinforcement learning.

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A practical Introduction to
Machine Learning in Python
Piero Casale

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www.ailab.si/orange/

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Load-In Data and Basic Data Exploration
- Loading Data:
iris = orange.ExampleTable('iris.tab')
- Exploring Features and Examples
iris.domain.attributes
iris.domain.classVar.name
- Basic Dataset Characteristics
GetDatasetStatistics()
- Dataset Formats in Orange:
csv, txt, xls
- Dataset as Python Lists:
indexing, append, extend, native

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Dataset Visualization
- Multi Dimensional Scaling:
MultiDimensional Scaling Functions in orngMDS

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My First Classifier in Orange : Bayes

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My First Classifier in Orange : Bayes
- Loading Data:
iris = orange.ExampleTable('iris.tab')
- Declare the Learning Function:
bayes = orange.BayesLearner()
- Train the Bayes Classifier on Data:
BayesClassifier = bayes(iris)
- Classify new data:
Prediction = bayesClassifier(newExample)
_ Example on Iris Dataset:
exCodes.showBayes()

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My (Second) Classifier in Orange :
Decision Trees
- As before:
import orngTree
treeLearner = orngTree.TreeLearner()
treeClassifier = treeLearner(iris)
prediction = treeClassifier(newExample)
_ Measures for splitting : infoGain, gainRatio, gini
treeLearner = orngTree.TreeLearner(measure='gini')
- Print the Tree:
- on screen : orngTree.printTree(treeClassifier)
- save as an image :
orngTree.printDot(treeClassifier, fileName='tree.dot')
dot -Tpng tree.dot -otree.png

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Testing and Evaluating a Classifier
- Testing Functions in orngTest
import orngTest
learners = [bayesLearner, treeLearner]
- Make a 10 folds Cross Validation
xv = orngTest.crossValidation(learners, data, folds=10)
- Scores Functions in orngStat
import orngStat
accuracy = orngStat.CA(xv)
confusionMatrix = orngStat.cm(xv)
- Example on Iris Dataset using Bayes, DecisionTree and Knn.
exCodes.crossValidate()

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Ensemble Methods
- Basic Ensemble Methods in orngEnsemble
Bagging, Boosting and Random Forest
import orngEnsemble
- Bagging of Decision Trees
treeLearner = orngTree.TreeLearner()
baggedTrees = orngEnsemble.BaggedLearner(treeLearner, t=10)
- Boosting of Decision Trees
treeLearner = orngTree.TreeLearner()
boostedTrees = orngEnsemble.BoostedLearner(treeLearner, t=10)
- Random Forest
forest = orngEnsemble.RandomForestLearner(trees = 10)
- Example on Iris Dataset:
exCodes.crossValidateEnsembles()

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Features Selection
- Functions for Features Selectoin in orngFSS
import orngFSS
vehicle = orange.ExampleTable('vehicle.tab')
- Measuring Import of features with Information Gain
measures = orngFSS.attMeasure(vehicle)
TenBests = orngFSS.bestNAtts(measures,n=10)
- Measuring Import of features with Gain Ratio
gainRatio = orange.MeasureAttribute_gainRatio()
measures = orngFSS.attMeasure(vehicle,gainRatio)
fiveBests = orngFSS.bestNAtts(measures,n=5)
- Example on Vehicle Dataset:
exCodes.measureAttributes()

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More.....
- Supervised Learning Algorithms:
orngSVM,orngLR,orngC45
- Unsupervised Learning Algorithm :
orngClustering
- Reinforcement Learning :
orngReinforcement
- Outlier Detection :
orngOutlier
- Discretization Functions :
orngDisc

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Enjoy.....
More at www.ailab.si/orange
Piero Casale