Table of Contents
What advantage is the discriminative model over the generative model?
They are also not capable of generating new data instances. Discriminative models have the advantage of being more robust to outliers, unlike the generative models. However, one major drawback is a misclassification problem, i.e., wrongly classifying a data point.
Why generative model is important?
Generative modeling is used in unsupervised machine learning as a means to describe phenomena in data, enabling computers to understand the real world. This AI understanding can be used to predict all manner of probabilities on a subject from modeled data.
What is generative classifier?
A generative classifier tries to learn the model that generates the data behind the scenes by **estimating the assumptions and distributions of the model. It then uses this to predict unseen data, because it assumes the model that was learned captures the real model.
What are generative and discriminative models in machine learning?
In simple words, a discriminative model makes predictions on the unseen data based on conditional probability and can be used either for classification or regression problem statements. On the contrary, a generative model focuses on the distribution of a dataset to return a probability for a given example.
What are generative and discriminative models?
What is probabilistic generative model in machine learning?
A generative model includes the distribution of the data itself, and tells you how likely a given example is. For example, models that predict the next word in a sequence are typically generative models (usually much simpler than GANs) because they can assign a probability to a sequence of words.
What is the difference between discriminative and generative machine learning?
Discriminative machine learning is actually training a model. To tell apart the right output among possible output choices. This is done by learning model parameters that maximize the conditional probability P (Y|X). Generative machine learning is training a model to learn parameters maximizing the joint probability of P (X, Y).
What is the difference between discriminative models and generative models?
1 Both can be used for classification. 2 Discriminative models are only for supervised learning problems, whereas Generative models apply to both supervised and unsupervised learning. 3 Discriminative models are known to outperform Generative models. 4 Generative models can provide rich data insights , when you do not have any labels.
What is generative machine learning (GML)?
Generative machine learning is training a model to learn parameters maximizing the joint probability of P (X, Y). Often learned in probabilistic models in its factorized form P (Y), P (X|Y) is simplified in most scenarios. With assumptions of conditional independence P (Y), P (X|Y).
What are machine learning models and types?
Machine learning models can be classified into two types of models – Discriminative and Generative models. In simple words, a discriminative model makes predictions on the unseen data based on conditional probability and can be used either for classification or regression problem statements.