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Unlocking the Power of Data: Embracing Machine Learning for Business Success - Part 3

 


Machine learning is a vast and ever-evolving field with a wide array of algorithms and techniques at its disposal. One fundamental way of categorizing these methods is based on the nature of the input-output relationship, particularly focusing on the probability model. The two primary categories of this categorization are Discriminative and Generative models.

Discriminative Models

Discriminative methods, as the name suggests, aim to discriminate or distinguish between different classes of data. These models directly learn the probability of an output, given an input. Imagine you have an image classification task, where you need to determine whether an image contains a cat or a dog. A discriminative model will predict the probability that the image is a cat versus a dog. Essentially, it acts as a forward model, making predictions based on input data.

One classic example of a discriminative model is Logistic Regression. It is often employed for binary classification tasks, where the objective is to predict one of two possible outcomes. For instance, in email classification, you can use logistic regression to predict whether an email is spam (1) or not spam (0) based on features like email length and the number of exclamation marks.

Other discriminative models include Scalar Vector Machine (SVMs), Traditional neural networks, Nearest neighbor, Conditional Random Fields (CRFs), and Decision Trees and Random Forests. Discriminative models do not make any assumptions about the data points; instead, they focus on separating one class from another.

However, it's worth noting that discriminative models are not capable of generating entirely new data points. They are designed to make predictions based on existing data rather than creating novel instances.

Generative Models

In contrast to discriminative models, generative models seek to model the joint probability of both input (x) and output (y) or sometimes the reverse probability. Instead of merely predicting outcomes, they aim to understand the underlying distribution of the data and generate new data samples that resemble the training data.

For example, imagine you have a dataset of 1D data points, such as the heights of people in a population. Generative models can be used to create new data points that closely resemble the distribution of heights in the dataset.

Some common types of generative models include Naïve Bayes, Bayesian networks, Markov random fields, Hidden Markov Models (HMMs), Latent Dirichlet Allocation (LDA), and the highly popular Generative Adversarial Networks (GANs).

In summary, discriminative models make predictions on unseen data based on conditional probabilities and are often used for classification or regression problems. They excel at distinguishing between classes. On the other hand, generative models focus on understanding the data distribution and can generate new, similar data points. They are valuable when you need to create new data that fits a particular pattern.


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