Microsoft Azure Machine learning Algorithms


 Malcolm Porter
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1 Microsoft Azure Machine learning Algorithms Tomaž
2 Our Sponsors
3 Speaker info https://tomaztsql.wordpress.com
4 Agenda Focus on explanation of algorithms available for predictive analytics in Azure Machine Learning service. Algorithms 1) regression algorithms, 2) TwoClass classifications, 3) Multiclass classification, 4) Clustering  Explore algorithm  which algorithm is used and useful for what kind of empirical problem  which is suitable for particular dataset.
5 Before start... Why this session?!
6 First things first
7 First things something By Type Categorical Numerical Nominal Ordinal Interval Ratio gender (male/ Female), eye colour (blue, green, black,...), marital status (S, M, D,V,...), Buyer Nonbuyer (0 1) No inherited order; all values are the same and all values equal in representation education (primary, secondary, high school,...), Salary (<101, , ,..) Ordering can be applied; values can be compared with =,>,< and recoded with numbers / classes. Height (173cm, 196cm), Invoice Value (24.4, 42.6,..) Ordering can be applied; values can be compared with =,>,< and expressed 2 is 2x times bigger.. Temperature: Celsius vs. Fahrenheit
8 For warmup...regression
9 First things second
10 Sample Data
11 1 General (interferance) statistics Apply Math Operation > Applies a mathematical operation to column values Compute Elementary Statistics > Calculates specified summary statistics for selected dataset columns Compute Linear Correlation > Calculates the linear correlation between column values in a dataset Descriptive Statistics > Generates a basic descriptive statistics report for the columns in a dataset Evaluate Probability Function > Fits a specified probability distribution function to a dataset Replace Discrete Values > Replaces discrete values from one column with numeric values based on another column Test Hypothesis Using ttest > Compares means from two datasets using a ttest Source: https://msdn.microsoft.com/enus/library/azure/dn aspx
12 1 General Statistics
13 1 Spliting data Splitting Mode:  Split rows  Recommender split  Regular / Relative expression Random seed Stratified split it depends on the size of our dataset. If it is large enough, 66% split is a good choice (66% for training and the others for test). if it is a moderated dataset, 10fold cross validation (or leaveoneout) can be a good choice. if your dataset is small then Bootstraping is good. Jackknife, cross validation, nfold cross validation,...
14 1 Spliting data
15 1 Initializing Model
16 1 Initializing Model
17 1 Initializing Model Making list of algorithms more transparent
18 Algorithms in Theory Regression Linear and Logistic Azure ML: Linear Regression Azure ML: Twoclass Classification Logistic Regression Multiclass classification Logistic Regression
19 Algorithms in Theory Decision Tree, Decision Forests, Decision Jungles Decision tree Decision Forest Decision Jungle Azure ML: Regression boosted decision tree Twoclass classification boosted decision tree Azure ML: Regression decision forrest Twoclass classification decision forrest Multi classification decision forrest Azure ML: Multiclass decision jungle Twoclass classification decision jungle
20 Algorithms in Theory Naive Bayes Azure ML: Regression Bayes linear Two Class classification Bayes point machine
21 Algorithms in Theory Neural networks and perceptrons Azure ML: Regression Neural networks Two Class classification Neural networks Multi Class classification Neural networks Two Class Classification averaged perceptrons
22 Algorithms in Theory SVM Azure ML: Two Class classification SVM Two Class classification locally deep SVM Anomaly detection SVM
23 1 Regression Algorithms
24 1 Regression Algorithms Parameters
25 1 Regression Algorithms
26 Evaluating Regression Algorithms Metrics to measure how close predictions are to eventual outcomes Metrics of differences between predicted values and actual values. Summarization of regression model how well fits a statistical model; R^2 = 1 model is perfect, respectively
27 Evaluating Regression Algorithms Predicting Salary
28 Comparison of Regression Algorithms Regression Algorithm Accuracy Training time Linearity Customization Predicting Variable Type of independant variable(s) Data Quantity linear Good Fast Excellent Good Interval Any small to big Bayesian linear Good Fast Excellent Moderate Interval Any big decision forest Excellent Moderate Good Good Interval Any boosted decision tree Excellent Fast Good Good Interval Any big fast forest quantile Excellent Moderate Moderate Excellent Distribution (Interval) Any neural network Excellent Slow Moderate Excellent Interval Any smaller Excellent* Poisson Good Moderate (log linear) Good Interval (counts) Any small to big ordinal Good Moderate Excellent None Ordinal (order) Any small to big Scale: Excellent Good Moderate Fast Moderate Slow
29 2 Twoclass Classification
30 2 Twoclass Classification parameters
31 Regularization weight
32 2 Twoclass Classification
33 2 Evaluating twoclass Classification AUC/ROC: <= WTF?
34 2 Evaluating twoclass Classification
35 Comparison of Twoclass Classification Algorithms Twoclass classification Accuracy Training time Linearity Customization Predicting Variable Type of independant variable(s) Data Quantity logistic regression Good Fast Excellent Good dichotomous / binary Any smallbig decision forest Excellent Moderate Good Good dichotomous / binary Any smallbig decision jungle Excellent Moderate Good Good dichotomous / binary Any big boosted decision tree Excellent Moderate Good Good dichotomous / binary Any big neural network Excellent Slow Moderate Excellent dichotomous / binary Any averaged perceptron Good Moderate Excellent Moderate dichotomous / binary Any support vector machine Excellent Moderate Excellent Good dichotomous / binary Any big locally deep support vector machine Good Slow Good Excellent dichotomous / binary Any big Bayes point machine Moderate Moderate Excellent Moderate dichotomous / binary Any Scale: Excellent Good Moderate Fast Moderate Slow
36 3 Multiclass Classification
37 3 Multiclass Classification parameters
38 3 Multiclass Classification
39 3 Evaluating multiclass Classification
40 3 Evaluating multiclass Classification
41 3 Comparison of Multiclass Classification Multiclass classification Accuracy Training time Linearity Customization Predicting Variable Type of independant variable(s) Data Quantity logistic regression Good Fast Excellent Good Nominal / ordinal (with 2+ classes) any smallbig decision forest Excellent Moderate Good Good Nominal / ordinal (with 2+ classes) any big decision jungle Excellent Moderate Good Good Nominal / ordinal (with 2+ classes) any big neural network Excellent Slow Moderate Excellent Nominal / ordinal (with 2+ classes) any small Scale: Excellent Good Moderate Fast Moderate Slow
42 4 Using Sweeping and SMOTE
43 4 Using Sweeping and SMOTE
44 5 Clustering
45 5 Clustering
46 5 Clustering
47 5 Evaluating Clustering
48 Key takeaways https://azure.microsoft.com/enus/documentation/articles/machinelearningalgorithmcheatsheet/ https://msdn.microsoft.com/enus/library/azure/dn aspx
49 How did you like it? Please give feedback to the event: to me as a speaker: sessionevaluation.aspx
50 THANKS!
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