Manage Data In Microsoft Azure Machine Learning

Click on to project columns to launch the column selector where you can select the desired values.

After clicking on launch column selector, the following pop up will open. Select columns from the left side of the page which you want to see in project columns, after selecting the desired columns click on the check button.

In some cases we are missing some data like age of some people, so in this situation we can use the clean missing data. Search the clean missing data facility from search box, drag and drop in the page.

Again connect your output to the input.

Then I need columns to be cleaned, select clean missing data and in the right side of the page click on "Launch column selector".

We want to select age from all the columns, click column names and in dropdown menu select age. Click on to Next button to complete the action.

The cleaning mode will replace with median as in the following figure,

The next thing to do, we want to split data. Just go into search box and search it. Then, connect the clean missing data box to the Split Data box. Click on the Split Data box and you will see that you can set a few parameters for it on the right of the screen. Enter “0.7” for “fraction of rows”.

Now, Find and drop “Train Model” from the menu. Connect the first data set from the Split Data to the second dataset of Train Model.

In train model there is a red mark, because of train model does not know how many columns we are going to use, so we need to tell it that. Click on Train Model and then “Launch column selector” from the right pane.

Start typing “Survived” into the column name box. A suggestion box will open. Select the first item named “Survived” in the list and click on check mark.

Now, I’m going to drop multiclass decision forest in to the working page, connect it with train model.

We have taken the necessary steps to train a model from our training data. Before going on with performance evaluation, let’s run our experiment to verify if it works or not.

Click on “Run” button at bottom of the page.

You will see that, one by one, green check boxes appear on the boxes on the working area. This means a calculation is performed inside the box and its results are ready for the next box to use.

For producing a model structure, right click on to Train Model and select “Visualize”.

Now, the “Train Model” box contains a trained model.

Now we need to drop a score model in the page to test our model to see how it performs. So go in to search box and type score model and drop it in to the working page.

Connect Train Model with first input of Score Model and the second output from Split Data to Score Model.

Now run your score model.

Right click on Score Model and Visualize it.

The result is shown in the following screenshot, where lowest fare is of Pclass.

Now add an Evaluate Model, and connect it from Score Model, Click on to Run button.

Right click on Evaluate Model and click on “Visualize”.

After this, in the following screenshot there are many types of accuracy completed tasks.

Under Metrics, you can see how well the algorithm performed by checking out “Overall Accuracy”. The value here shows what percentage of the digits in the cross validation set we have correctly predicted. In our case, it is somewhere around 67 percent. This means that we have learned a model that can be used to recognize handwritten digits with 67 percent accuracy.

I have tried to show how you can easily use Azure Machine Learning Studio for basic easy tasks.

I hope you enjoyed this article. Stay tuned with me for more article on Azure and other Microsoft technologies.

Thanks.

Connect (“Nitin Pandit”);