# A Simplified and Intuitive Categorisation of Discriminative Models

### A popular interview question.

In yesterday’s post, we discussed **Generative and Discriminative Models.**

Here’s the visual from that post for a quick recap:

In today’s post, we shall dive into a further categorization of **discriminative models**.

### A Quick Recap

**Discriminative models:**

learn decision boundaries that separate different classes.

maximize the conditional probability:

`P(Y|X)`

— Given an input X, maximize the probability of label Y.are meant explicitly for classification tasks.

**Generative models:**

maximize the joint probability:

`P(X, Y)`

learn the class-conditional distribution

`P(X|Y)`

are

**typically**not meant for classification tasks, but they can perform classification nonetheless.

Check yesterday’s post for more details: **Generative and Discriminative Models.**

### Further distinction of discriminative models

In a gist, discriminative models directly learn the function *f* that maps an input vector (`x`

) to a label (`y`

).

They can be further divided into two categories:

Probabilistic models

Direct labeling models

The following visual neatly summarizes their differences:

**Probabilistic models**

As the name suggests, probabilistic models provide a probabilistic estimate for each class.

They do this by learning the posterior class probabilities `P(Y|X)`

— given an input `X`

, what is the probability of a label `Y`

.

As a result, their predictions depict the model’s confidence in predicting a specific class label.

This makes them well-suited in situations when uncertainty is crucial to the problem at hand.

Examples include:

Logistic regression

Neural networks

Conditional Random Fields (CRFs)

### Labeling** models**

In contrast to probabilistic models, labeling models (**also called distribution-free classifiers**) directly predict the class label, **without providing any probabilistic estimate.**

As a result, their predictions DO NOT indicate a degree of confidence.

This makes them unsuitable when uncertainty in a model’s prediction is crucial.

Examples include:

Random forests

kNN

Decision trees

That being said, it is important to note that these models, in some way, can be manipulated to output a probability.

For instance, Sklearn’s decision tree classifier does provide a `predict_proba()`

method, as shown below:

This may appear a bit counterintuitive at first.

However, in this case, the model outputs the class probabilities by looking at the fraction of **training class labels** in a leaf node.

In other words, say a test instance reaches a specific leaf node for final classification. The model will calculate the probabilities as the fraction of **training class labels** in that leaf node.

Yet, these manipulations do not account for the “true” uncertainty in a prediction.

This is because the uncertainty is the same for all predictions that land in the same leaf node.

Therefore, it is always wise to choose probabilistic classifiers when uncertainty is paramount.

👉 Over to you: Can you add one more model for probabilistic and labeling models?

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About the probabilities computed from a decision tree you say: "Yet, these manipulations do not account for the “true” uncertainty in a prediction. This is because the uncertainty is the same for all predictions that land in the same leaf node." Can you explain this a bit further? What is the problem with this type of uncertainty?

Just to confirm XGBOOST and LightGBM are labelling models right? Can we say all ensemble based models are labelling models?