Weka steps for logistic regression

Machine learning in WEKA

Transcript

1. Modeling using WEKA
2. 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
3. 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
4. Dataset File formats
5. Load Data Set
6. Univariate Analysis
7. Univariate Analysis • Current Relation – Dataset name, number of records, number of attributes in dataset. • Attribute details- All attributes to select for univariate analysis
8. Univariate Analysis • Selected attribute – • Provides information about attribute type, Missing values, Distinct values, etc.
9. Univariate Analysis • Selected attribute – histogram • Dispersion of attribute.
10. Univariate Analysis • All attribute visualization/plots
11. Data Manipulation • Changing data type of field • Missing values update • Creating BINS from data • Standardize data • Outlier Treatment • Creating new calculated fields
12. 1. Convert NA to 0 • Flow > Preprocess > Edit > Right Click on Attribute > Replace Values
13. 1. Convert NA to 0
14. 2. Changing data types of attribute • Flow > Preprocess > Filter > Choose > Filters > Unsupervised > Attribute >
15. 2. Changing data types
16. 3. Creating BINS • Flow > Preprocess > Filter > Choose > Filters > Unsupervised > Attribute > Discretize
17. 3. Creating BINS • Provide attribute number, Number of BINs to be created and click on ‘Apply’
18. 3. Creating BINS • Click on attribute to see the bins and distribution •
19. 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)
20. 4. Standardize data • To convert all numeric attributes in data to zero mean and unit variance. • Flow > Preprocess > Filter > Choose > Filters > Unsupervised > Attribute > Standardize •
21. 4. Standardize data
22. 4. Standardize data- Log values • To convert specific numeric attributes to log. • Flow > Preprocess > Filter > Choose > Filters > Unsupervised > Attribute > Numeric Transform •
23. 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 •
24. 5. Identify Outliers • Flow > Preprocess > Filter > Choose > Filters > Unsupervised > Attribute > Interquartile Range • Outliers can be identified for separate attribute or for all together
25. 5. Identify Outliers
26. 5. Remove Outliers • Flow > Preprocess > Filter > Choose > Filters > Unsupervised > Instance > RemoveWithValues
27. 5. Remove Outliers • Params : attributeIndices - Attribute number, NominalIndices=Nominal value of outlier in Outlier attribute
28. 5. Transform Outliers • Flow > Preprocess > Filter > Choose > Filters > Unsupervised > Attribute > AddExpression – this option will create new field e.g : ifelse(a2 > 1000,200, 1) •
29. 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
30. 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
31. Feature Selection/Attribute selection 1. Info Gain 2. Correlation
32. Feature Selection – Info Gain • Flow > Select Attribute > Attribute evaluator > Choose >
33. Feature Selection – Correlation • Flow > Select Attribute > Attribute evaluator > Choose >
34. Features for Model • Features selected for model
35. Creating Training, Validation and Test Sets
36. Creating data sets 1. Dividing data into 60-20-20 % (Train-Test-Evaluate) 2. Weka inbuilt methods
37. Creating data sets • For 60%-20%-20% • Step 1- • Flow > Preprocess > Filter > Choose > Unsupervised > Instance > Resample
38. Creating data sets • Step 2- Parameters for resample • Flow > Preprocess > Filter > Choose > Unsupervised > Instance > Resample > • Check noReplacement = True, sample size percent – 60 > ok > Apply
39. 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
40. Creating data sets • Step 7 – Apply and check results as below
41. 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
42. 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
43. Creating data sets • .
44. 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
45. 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
46. 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
47. 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
48. 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
49. Logistic Regression
50. Logistic Regression • Flow > Classify > Functions > Logistic
51. Logistic Regression • Parameter selection
52. Logistic Regression • Model Results:
53. Model Analysis • Flow > Right Click on Model > Visualize threshold curve > ROC Curve
54. Model Analysis- ROC Curve
55. Model Analysis- Cost Benefit Analysis • Flow > Classify > Right click model > Cost/Benefit Analysis
56. 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
57. 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
58. Re-apply model on new data
59. 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.
60. 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.
61. THANK YOU !
62. Decision Tree
63. Decision Tree: Algorithm selection
64. Decision Tree: Setting params for algo
65. Decision Tree: Execution
66. Tree Visualization