This is a simple example about optimizing prediction models on Azure. In this case we will use a Boosted Decision Tree model. We will show you how you can use the Permutation Feature Performance module to prune your trees.
We start with the Student Performance Classifier from a previous blog. We already found out that the Boosted Decision Tree algorithm gave the best results, so we will start with that one to train our model with. Continue reading “Optimizing prediction models on Azure – pruning the trees”
This blog explains how to build a Human Activity Classifier with Azure Machine Learning. This classifier predicts somebody’s activity class based on the use of wearable sensors. The complete experiment can be downloaded from the Azure Machine Learning Gallery. Continue reading “How to build a Human Activity Classifier with Azure Machine Learning”
Predictive maintenance can be quite a challenge :). Some of you might have tried to build the Azure ML Predictive Maintenance Template by Microsoft.
In this template, you are guided through the steps that are required to build and deploy several predictive maintenance scenarios. These steps are offered as experiments in the Cortana Analytics Gallery and can be easily downloaded. The original data comes from the NASA prognostic data repository.
This blog focusses on step 1: Predictive Maintenance: Step 1 of 3, data preparation and feature engineering Continue reading “Azure ML Predictive Maintenance Template – data preparation and feature engineering”
This blog explains how to build a Student Performance Classifier with Azure Machine Learning. This classifier predicts if a student will pass or fail Mathematics. The complete experiment can be downloaded from the Azure Machine Learning Gallery. Continue reading “How to build a Student Performance Classifier with Azure Machine Learning”
Improving your Azure Machine Learning model
In this example we start with a sample experiment from the Microsoft Azure Machine Learning Gallery: Regression: Demand estimation. In this example there are four models built, and compared, based the newly created features. We will explore whether standard operations could improve these samples models, inspired by the e-book Data Science in the Cloud with Azure Machine Learning and R of Stephen Elston. We haven’t used the suggested new features that depend on prior info which wasn’t always complete, but created some other variables, that could be created based on the available dataset. Observing the sample, there are basically two areas where we see quick possibilities for improvement: data cleaning and transformation and evaluation of the results. Continue reading “Azure Machine Learning: how to easily improve your model”