Azure Machine Learning - Everything You Need to Know

This blog will be focusing on Azure ML Studio since it is convenient to use as compared to Azure ML Service. Let’s now talk about the working of Azure ML Studio.

As mentioned earlier, Azure ML Studio uses a drag-and-drop feature which does not need any coding. There are predefined algorithms and sample datasets that we can work with.

But, how to choose the right algorithm? Microsoft provides a cheat sheet that we can download for deciding on the right algorithm. Otherwise, here is how we do it:

• If we want to predict value, we can use regression. We can forecast the future by estimating the relationship between variables. Examples are product demand estimation, sales figures prediction, equipment servicing priorities determination, etc. The algorithm options are:

1. Ordinal regression
2. Poisson regression
3. Fast Forest Quantile regression
4. Linear regression
5. Bayesian regression
6. Neural network regression
7. Decision forest regression
8. Boosted decision tree regression

• If we want to identify and predict rare data points, our options are:

1. One class SVM
2. PCA-based anomaly detection

Some examples of anomaly detection are fraud detection, abnormal equipment readings, etc.

• If we want to group similar data into one set, K-means clustering is the algorithm we should use. Examples are customer taste prediction, customer segmentation, etc.
• If we want to predict between two categories (like to predict whether a tweet is positive, to which the answer would be either ‘yes’ or ‘no’), the algorithms to use are:

   1. Two-class SVM
  2. Two-class averaged perceptron
  3. Two-class Bayes point machine
  4. Two-class decision forest
  5. Two-class logistic regression
  6. Two-class boosted decision tree
  7. Two-class decision jungle
  8. Two-class locally deep SVM
  9. Two-class neural network

• If we want to predict between multiple categories (such as finding the mood of a tweet), then here are the options:

  1. Multiclass logistic regression
  2. Multiclass neural network
  3. Multiclass decision forest
  4. Multiclass decision jungle
  5. One-vs-all multiclass

With the basics being cleared, let us now take up an example.

It is recommended to create an Azure account beforehand. The first 12 months are free with 13,300 credits so that we can practice. Here, the example is of Predicting Diabetes for people depending on different fields. In this example, we have to predict if a person has diabetes or not. We can also check the accuracy, precision, and F1 score of the model. In this hands-on, we are going to select a dataset that is already available and use the two-class regression algorithm for training the dataset.

Here is the step-by-step process of building the prediction model

Step 1: Search for Azure Machine Learning Studio on Google and click on the first link. Login with the credentials and we can see the studio. To create a new experiment, click on NEW which is on the bar at the bottom of the studio

Step 2: When we click on NEW, here are the options that will pop up. Click on Blank Experiment and we will be redirected to our workspace, where we can start with our experiment

Step 3: Before moving on, rename the experiment. As the example is on predicting if a person has diabetes or not, here, the experiment will be renamed as Diabetes prediction. Here is how it would look like:

Step 4: Now, select a dataset. There are many sample datasets available for experiment. Take the sample dataset of Diabetes binary classification

1. From the menu on the left, select Saved Datasets

2. For more insights on sample datasets, click on Samples and go through the list of sample datasets available

3. Select Pima Indian Diabetes Binary Classification Dataset, drag it to the center of the screen and drop it

Step 5: Now that we have our dataset, let us see what it has.

1. Click on ①

2. And from the options, click on Visualize

Once we click on Visualize, we will be able to see what our data looks like. Here is our data that we will be working with:

Step 6: Now, we have to select columns that are relevant to train our model. Here is how we do it:

1. On the left-hand side, there is a search bar, where we will search for Select Columns in Dataset

2. Drag and drop the item below the dataset and connect the two

3. On the right side, there is a box that says Launch column selector. Click on it to select columns

Step 7: After clicking on that entity, we can see a screen popping up. In this example, all columns are relevant and, hence, we will select all of them

1. Select all items

2. Click on the arrow that points right. This indicates that columns are selected

3. Navigate below and click on the Tick mark

4. Close the window

Step 8: Now that we have selected the columns we want to train our model on, we need to split the data into training and test datasets

1. For that, search for Split Data and drop it on the workspace

2. Join Column selector and split data

3. Toward the right, we can change the percentage of train and test datasets. It is initially 0.5, but we want 70 percent training data and 30 percent test data. Hence, we will change it to 0.7. We can make the ratio 80-20 as well or as per our requirement

In this, ① is our training dataset and ② is our test dataset.

Step 9: We now have our training and testing datasets. Next, we need an algorithm to train our model. The algorithm we are choosing is two-class logistic regression. Logistic regression is used to predict the probability of an outcome. It predicts the probability of the appearance of an event by providing data to a logistic function.
Since there are two outcomes, it is two-class logistic regression. For a single value, we can use linear regression.
Also, our aim of this prediction model is to find if a person is diabetic or not. Hence, this falls under classification. Search for Classification, and under the category we will be able to find this algorithm

Step 10: It is now time for us to train our model

1. Search for Train Model and drop it on the workspace

2. Connect the Algorithm to the train model.

3. Connect the training dataset from split data to train model

4. Then, we need to select the column that we need to test

Step 11: Now, we need to score our trained model and then evaluate it

1. Drag and drop the Score Model on to the center

2. Connect the Train Model and ②

3. Connect the Training Dataset from Split Data to train model

4. Drag and drop Evaluate Model

5. Connect Score Model with it

6. Save the experiment

7. Run the experiment

Step 12: After running the model, we need to visualize the result and find precision, accuracy, etc.

1. Click on Evaluate and then on ①

2. Select Visualize and there will be a pop-up window like below

Here are the results:

In case we want to set the threshold, we can do so by moving the scale.

Note: Usually, False Positive and False Negative should be minimum. In this example, the most important entity in the confusion matrix is the number of False Negatives. False Negative is when a person has diabetes but the prediction was that he/she does not have it, which is more fatal. So, set the threshold in a way that you get a minimum number of False Negatives.
To give a clear picture of the confusion matrix, here is what you need to know:

Let us say you have taken a test to detect diabetes. Now, your result is either positive or negative. It is positive if you have diabetes and negative if you don’t. With that in mind, check out the conditions:

TP: When someone has diabetes and the predicted result is positive.
FN: When someone has diabetes and the predicted result is negative.
FP: When someone does not have diabetes and the predicted result is positive.
TF: When someone does not have diabetes and the predicted result is negative.

Now, we have come to an end of this blog. Simple, isn’t it? It is way easier to drag and drop instead of coding every bit. We can also upload our datasets from local files and run different Machine Learning algorithms on it.
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