Azure ML - Experiments

Azure ML - Experiments

1. Saved Dataset

   • My Datasets
   • Samples

2. Data Format Conversions

   • Convert to ARFF
   • Convert to CSV
   • Convert to Dataset
   • Convert to SVMLight
   • Convert to TSV

3. Data Input and Output

   • Enter Data Manually
   • Export Data
   • Import Data
   • Load Trained Model
   • Unpack Zipped Datasets

4. Data Transformation

   • Filter
   • Learning with Counts
   • Manipulation
   • Sample and Split
   • Scale and Reduce

5. Feature Selection

   • Filter Based Feature Selection
   • Fisher Linear Discriminant Analysis
   • Permutation Feature Importance

6. Machine Learning

   • Evaluate
   • Initialize Model
   • Score
   • Train

7. OpenCV Library Modules

   • Import Images
   • Pre-trained Cascade Image Classification

8. Python Language Modules

   • Execute Python Script

9. R Language Modules

   • Create R Model
   • Execute R Script

10. Statistical Functions

    • Apply Math Operation
    • Compute Elementary Statistics
    • Compute Linear Correlation
    • Evaluate Probability Function
    • Replace Discrete Values
    • Summarize Data
    • Test Hypothesis using t-Test

11. Text Analytics

    • Detect Languages
    • Extract Key Phrases from Text
    • Extract N-Gram Features from Text
    • Feature Hashing
    • Latent Dirichlet Allocation
    • Named Entity Recognition
    • Preprocess Text
    • Score Vowpal Wabbit Version 7-10 Model
    • Score Vowpal Wabbit Version 8 Model
    • Train Vowpal Wabbit Version 7-10 Model
    • Train Vowpal Wabbit Version 8 Model

12. Time Series

    • Time Series Anomaly Detection

13. Web Service

    • Input
    • Output

14. Deprecated

    • Apply Quantization Function
    • Assign to Clusters
    • Build Count Table
    • Count Featurizer
    • Linear Discriminant Analysis
    • Missing Values Scrubber
    • Quantize
    • Score Vowpal Wabbit Version 7-4 Model
    • Train Vowpal Wabbit Version 7-4 Model