Statistical Analysis

Back to basics: Multi Class Classification vs Two class classification.

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Classification algorithms are commonly used to build predictive models. Here’s what they do (simplified!):

Machine Learning Predictive Algorithms analytics Introduction

Now, here’s the difference between Multi Class and Two Class:

if your Test Data needs to be classified into two classes then you use a two-class classification model.

Examples:

1. Is it going to Rain today? YES or NO

2. Will the buyer renew his soon-to-expire subscription? YES or NO

3. What is the sentiment of this text? Positive OR Negative

As you can see from above examples the test data needs to be classified in two classes.

Now, look at example #3 – What is the sentiment of the text? What if you also want an additional class called “neutral” – so now there are three classes and we’ll need to use a multi-class classification model. So, If your test data needs to be classified into more than two classes then you use a multi-class classification model.

Examples:

1. Sentiment analysis of customer reviews? Positive, Negative, Neutral

2. What is the weather prediction for today? Sunny, Cloudy, Rainy, Snow

I hope the examples helped, so next time you have to choose between multi class and two class classification models, ask yourself – does the problem ask you to predict two classes or more? based on that, you’ll need to pick your model.

Example: Azure Machine Learning (AzureML) studio’s classifier list:

Azure Machine Learning classifiers list

I hope this helps!

Statistics 101: Nominal, Ordinal, Interval, Ratio Data

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If you work with any statistical analysis tool, sometimes you may have run into configuring the data into either of these following categories: Nominal, Ordinal, Interval, Ratio

Here is what each term means:

NominalSimply names or call them set of charactersExample: Full name, fruits, cars, etc
OrdinalNominal + They have orderExample: Small, medium, big
IntervalOrdinal + the intervals between each value are equally splitExample: temperature in Fahrenheit scale:10 20 30 etc

Note that 20F is not twice as cold as 40F. So multiplication does not make sense on Interval data. But addition and subtraction works. Which brings us to next point: Ratio

RatioInterval + multiplication makes senseWeight: 60KG, 120KG.120 KG = 2 * 60 KG

I hope the examples are of help when you are working with statistical analysis tools and need to categorize the data.