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bamurtaugh and CESARDELATORRE Migration/v1.3.1 (#597)
* Add anomaly detection example to solution

* Updated label/score printing for anomaly detect

With ML.NET v1.3.0, fixed issue where Predicted Label was always true. No longer need "hack" of comparing score to 0.2

* Update build props nuget versions

ML.NET v1.3.1, ML Preview 0.15.0

* Renamed solution to match v1.3.1

* Update C# readmes to v1.3.1

* Update F# E2E readme

* Update F# getting started readmes

Change to v1.3.1

* Rename F# solution to v1.3.1

* Update to preview v0.15.1

* Changed to ML from MLPreview

Update TimeSeries to v1.3.1 instead of preview

* Update timeseries from preview to regular v1.3.1

* Change TimeSeries from preview to regular v1.3.1

* Update TensorFlow from Preview to regular v1.3.1

* Update TensorFlow from preview to regular v1.3.1
Latest commit 2feb479 Aug 6, 2019
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BikeSharingDemand Migrate Samples to 1.0.0 (#404) May 3, 2019
BikeSharingDemand.sln BikeSharingDemand with Common-code approach Nov 2, 2018 Migration/v1.3.1 (#597) Aug 6, 2019

Bike Sharing Demand - Regression problem sample

ML.NET version API type Status App Type Data type Scenario ML Task Algorithms
v1.3.1 Dynamic API Up-to-date Console app .csv files Demand prediction Regression Fast Tree regressor compared to additional regression algorithms

In this sample, you can see how to use ML.NET to predict the demand of bikes. Since you are trying to predict specific numeric values based on past observed data, in machine learning this type of method for prediction is known as regression.


For a more detailed descritpion of the problem, read the details from the original Bike Sharing Demand competition from Kaggle.


The original data comes from a public UCI dataset:

ML task - Regression

The ML Task for this sample is a Regression, which is a supervised machine learning task that is used to predict the value of the label (in this case the demand units prediction) from a set of related features/variables.


To solve this problem, you build and train an ML model on existing training data, evaluate how good it is (analyzing the obtained metrics), and lastly you can consume/test the model to predict the demand given input data variables.

Build -> Train -> Evaluate -> Consume

However, in this example we train multiple models (instead of a single one), each one based on a different regression learner/algorithm and finally we evaluate the accuracy of each approach/algorithm, so you can choose the trained model with better accuracy.

The following list are the trainers/algorithms used and compared:

  • Fast Tree
  • Poisson Regressor
  • SDCA (Stochastic Dual Coordinate Ascent) Regressor
  • FastTreeTweedie
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