Weka steps for logistic regression

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Modeling using WEKA

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Index • WEKA Introduction • WEKA file formats • Loading data • Univariate analysis • Data Manipulation • Feature Selection • Creating Training, Validation and Test Sets • Model Execution - Logistic Regression • Model Analysis - ROC Curve • Model Analysis – Cost/Benefit Analysis • Re-apply model on new data • Weka Plus and Negatives

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Introduction • Weka is a collection of machine learning algorithms for data mining tasks • The algorithms can either be applied directly to a dataset or called from your own Java code. • ARFF – Attribute relation file format

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Dataset File formats

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Load Data Set

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Univariate Analysis

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Univariate Analysis • Current Relation – Dataset name, number of records, number of attributes in dataset. • Attribute details- All attributes to select for univariate analysis

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Univariate Analysis • Selected attribute – • Provides information about attribute type, Missing values, Distinct values, etc.

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Univariate Analysis • Selected attribute – histogram • Dispersion of attribute.

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Univariate Analysis • All attribute visualization/plots

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Data Manipulation • Changing data type of field • Missing values update • Creating BINS from data • Standardize data • Outlier Treatment • Creating new calculated fields

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1. Convert NA to 0 • Flow > Preprocess > Edit > Right Click on Attribute > Replace Values

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1. Convert NA to 0

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2. Changing data types of attribute • Flow > Preprocess > Filter > Choose > Filters > Unsupervised > Attribute >

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2. Changing data types

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3. Creating BINS • Flow > Preprocess > Filter > Choose > Filters > Unsupervised > Attribute > Discretize

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3. Creating BINS • Provide attribute number, Number of BINs to be created and click on ‘Apply’

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3. Creating BINS • Click on attribute to see the bins and distribution •

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3. Creating Custom BINS • Flow > Preprocess > Filter > Choose > Filters > Unsupervised > Attribute > AddExpression • ifelse(a2 > 0, ifelse(a2 > 10,ifelse(a2 > 20,4, 3), 2), 1)

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4. Standardize data • To convert all numeric attributes in data to zero mean and unit variance. • Flow > Preprocess > Filter > Choose > Filters > Unsupervised > Attribute > Standardize •

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4. Standardize data

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4. Standardize data- Log values • To convert specific numeric attributes to log. • Flow > Preprocess > Filter > Choose > Filters > Unsupervised > Attribute > Numeric Transform •

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4. Standardize data- Log values • Provide value for attribute number which is to be converted to log value. • Also provide method name – log. Here we can provide any other methods such as abs,round,floor •

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5. Identify Outliers • Flow > Preprocess > Filter > Choose > Filters > Unsupervised > Attribute > Interquartile Range • Outliers can be identified for separate attribute or for all together

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5. Identify Outliers

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5. Remove Outliers • Flow > Preprocess > Filter > Choose > Filters > Unsupervised > Instance > RemoveWithValues

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5. Remove Outliers • Params : attributeIndices - Attribute number, NominalIndices=Nominal value of outlier in Outlier attribute

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5. Transform Outliers • Flow > Preprocess > Filter > Choose > Filters > Unsupervised > Attribute > AddExpression – this option will create new field e.g : ifelse(a2 > 1000,200, 1) •

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6. New Calculated fields • This is helpful In case any new field is to be derived from existing fields • Flow > Preprocess > Filter > Choose > Filters > Unsupervised > Attribute > Add Expression

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6. New Calculated fields • This is helpful In case any new field is to be derived from existing fields • Provide expression/equation and new field name

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Feature Selection/Attribute selection 1. Info Gain 2. Correlation

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Feature Selection – Info Gain • Flow > Select Attribute > Attribute evaluator > Choose >

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Feature Selection – Correlation • Flow > Select Attribute > Attribute evaluator > Choose >

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Features for Model • Features selected for model

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Creating Training, Validation and Test Sets

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Creating data sets 1. Dividing data into 60-20-20 % (Train-Test-Evaluate) 2. Weka inbuilt methods

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Creating data sets • For 60%-20%-20% • Step 1- • Flow > Preprocess > Filter > Choose > Unsupervised > Instance > Resample

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Creating data sets • Step 2- Parameters for resample • Flow > Preprocess > Filter > Choose > Unsupervised > Instance > Resample > • Check noReplacement = True, sample size percent – 60 > ok > Apply

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Creating data sets • Step 3 – Check • After apply we can check the current relation for number of records selected • • Step 4 – Save the result as filename_train.arff • Step 5 – Click on ‘Undo’ to get to original data set • Step 6 – Change the Resample parameters again • Parameters - > Invert selection = True, noReplacement = True, sampleSizePercent = 60

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Creating data sets • Step 7 – Apply and check results as below

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Creating data sets • Step 8 – Don’t save the results • Step 9 – Open Resample parameters set below parameters • Invert selection = False, noReplacement = True, sampleSizePercent = 50 • OK > Apply Check the results

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Creating data sets • Step 10 – Check the results • Step 11 – Save the results as Test Data. • Step 13 – Click on Undo to get earlier 40% of dataset • Step 13 – Parameters invertSelection = True, noReplacement=True , sampleSizePercent = 50 • Step 14 – Ok > Apply • Step 15 – Save as Evaluation Data

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Creating data sets • .

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Creating data sets 2. Weka inbuilt – Flow > Classify > Test Options > Use Training Set – With this option selected data set will be used as training set to create model

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Creating data sets 2. Weka inbuilt – Flow > Classify > Test Options > Use Supplied Test Set – With this option selected data set will be used as test set to create model

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Creating data sets 2. Weka inbuilt – Flow > Classify > Test Options > Use Supplied Test Set – With this option selected data set will be used as test set to test model

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Creating data sets 2. Weka inbuilt – Flow > Classify > Test Options > Cross Validation – With this option selected data set will be divided into 10 folds create model internally and weka will take average of all these models to show final model on UI

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Creating data sets 2. Weka inbuilt – Flow > Classify > Test Options > Percentage Split – With this option selected data set will be divided into Training and Test set for model creation

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Logistic Regression

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Logistic Regression • Flow > Classify > Functions > Logistic

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Logistic Regression • Parameter selection

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Logistic Regression • Model Results:

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Model Analysis • Flow > Right Click on Model > Visualize threshold curve > ROC Curve

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Model Analysis- ROC Curve

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Model Analysis- Cost Benefit Analysis • Flow > Classify > Right click model > Cost/Benefit Analysis

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Model Analysis- Cost/Benefit Analysis • Flow > Classify > Right click model > Cost/Benefit Analysis > Threshold Bar • Sliding the bar under Threshold label will change the accuracy and threshold curve

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Save prediction output to file • Flow > Classify > Test Options > More Options > Output Predictions > Text Bar to provide file name • Parameters: Choose: to provide file type, Attributes : First-last to get all fields, outPutFile : File name to save data

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Re-apply model on new data

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WEKA Pluses: • Platform independent and portable, java library can be invoked from any program in any language • User friendly GUI, with built in visualization, Simpler to use than R, large collection of different data mining algorithms • Better results for classification and cluster modeling • Ease of designing solutions. • Provides 3 ways to use the software: the GUI, a Java API, and a command line interface (CLI) • Can work with Spark, BigData using other packages on Experimenter or in batch mode.

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WEKA Limitation: • Visualizations can be managed better in R with different packages like ggplot • Not really flexible for data manipulation • Accepts only limited file format lile CSV,ARFF • Limited documentation available on Explorer.