MACHINE LEARNING | MULTICLASS CLASSIFICATION

MACHINE LEARNING - DAY 9

MULTI-CLASS CLASSIFICATION: ONE-VS-ALL

For the basics, you can check the earlier articles.

Terms used in this article can be understood from:

DAY 6: CLASSIFICATION AND LOGISTIC REGRESSION

Continuing our learning in machine learning today we’ll learn about the multi-class classification in logistic regression also known as one vs all.

Till now we have discussed about the 2 classification possibilities or 2 outcomes i.e., 1 or 0. Now, let’s see what happens when there are more number of possibilities.

for eg.,

lWeather: sunny, rainy, pleasant, windy

The outcome or the categorical value can be: 0, 1, 2, 3

lHealth: ill, dizzy, well

  

   The outcome or the categorical value can be: 0, 1, 2

The numbering doesn’t matter. It can be 1,2,3,4 or 0,1,2,3. These are just values which categorizes the given data or output into different categories.

y ∈ {0,1,2…,n}

h_Θ⁽⁰⁾(x) =  P(y = 0 | x; Θ )

h_Θ⁽¹⁾(x) =  P(y = 1 | x; Θ )

.

.

.

h_Θ⁽ⁿ⁾(x) =  P(y = n | x; Θ )

prediction : max(h_Θ⁽ⁱ⁾(x))

           i

STEPS OF COMPUTATION:

1. Plot the data

2. Take the classes one by one and rest of the 2 classes will behave as a single class or category. The probability of the single class is calculated in this way.

   For eg,

CONCLUSION:

Train a logistic regression h_Θ(x) for each class to predict the probability that y = i.

To make a prediction on a new x, pick the class that maximizes h_Θ(x) and that will be the output.

That’s all for day 9. Today we learned about the multi-class classification and how to compute it.

In day 10, we will be learning about the issue known as Overfitting which originates due to over-training of the model. The solution for this issue is Regularization which we’ll also cover in the next article.

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Till then Happy Learning!!!