** Is naive Bayes classifier or regression **

Naive Bayes is a supervised classification algorithm that is used primarily for dealing with binary and multi-class classification problems, though with some modifications, it can also be used for solving regression problems.

** Why Naive Bayes is not a good regressor **

We then present empirical evidence that isolates naive Bayes' independence assumption as the culprit for its poor performance in the regression setting. These results indicate that the simplistic statistical assumption that naive Bayes makes is indeed more restrictive for regression than for classification.

** What is the difference between classifier and regressor **

The key distinction between Classification vs Regression algorithms is Regression algorithms are used to determine continuous values such as price, income, age, etc. and Classification algorithms are used to forecast or classify the distinct values such as Real or False, Male or Female, Spam or Not Spam, etc.

** What is the difference between a Naive Bayes and a Logistic regression model **

Naive bayes give a faster solution for few training sets while considering independent features. Logistic regression has low bias and higher variance. Functional form indirect manner is used to predict the probability with categorical and continuous variables making the result set to be categorical.

** What kind of classifier is naive Bayes **

supervised machine learning algorithm

The Naïve Bayes classifier is a supervised machine learning algorithm, which is used for classification tasks, like text classification. It is also part of a family of generative learning algorithms, meaning that it seeks to model the distribution of inputs of a given class or category.

** Can naive Bayes be used for linear regression **

The comparison with linear regression depends on the error measure: for one measure naive Bayes performs similarly, while for another it is worse. We also show that standard naive Bayes ap- plied to regression problems by discretizing the target value performs similarly badly.

** Is KNN a regression or classification **

As we saw above, the KNN algorithm can be used for both classification and regression problems. The KNN algorithm uses 'feature similarity' to predict the values of any new data points. This means that the new point is assigned a value based on how closely it resembles the points in the training set.

** Why not use naive Bayes classifier **

Disadvantages of Using Naive Bayes Classifier

In most situations, the feature show some form of dependency. Zero probability problem : When we encounter words in the test data for a particular class that are not present in the training data, we might end up with zero class probabilities.

** Is XGBoost a classifier or Regressor **

What is XGBoost XGBoost, which stands for Extreme Gradient Boosting, is a scalable, distributed gradient-boosted decision tree (GBDT) machine learning library. It provides parallel tree boosting and is the leading machine learning library for regression, classification, and ranking problems.

** Is logistic regression a regressor or a classifier **

Logistic regression is a classification technique borrowed by machine learning from the field of statistics. Logistic Regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome.

** What is the difference between Bayesian and regression **

For example, in linear regression we chose values for the weights and bias that minimized our mean squared error cost function. In the Bayesian approach we don't work with exact values but with probabilities. This allows us to model the uncertainty in our parameter estimates. Why is this important

** Is decision tree a regression or classification **

Decision trees is a type of supervised machine learning algorithm that is used by the Train Using AutoML tool and classifies or regresses the data using true or false answers to certain questions.

** Is Naive Bayes a discriminative classifier or generative classifier **

Naive bayes is a Generative model whereas Logistic Regression is a Discriminative model . Generative model is based on the joint probability, p( x, y), of the inputs x and the label y, and make their predictions by using Bayes rules to calculate p(y | x), and then picking the most likely label y.

** Is Naive Bayes a linear classifier **

Naive Bayes is a linear classifier

Naive Bayes leads to a linear decision boundary in many common cases. Illustrated here is the case where P(xα|y) is Gaussian and where σα,c is identical for all c (but can differ across dimensions α). The boundary of the ellipsoids indicate regions of equal probabilities P(x|y).

** Is Naïve Bayes a linear classifier **

Naive Bayes is a linear classifier

Naive Bayes leads to a linear decision boundary in many common cases. Illustrated here is the case where P(xα|y) is Gaussian and where σα,c is identical for all c (but can differ across dimensions α). The boundary of the ellipsoids indicate regions of equal probabilities P(x|y).

** Is CNN a regression or classification **

Summary. In today's tutorial, you learned how to train a Convolutional Neural Network (CNN) for regression prediction with Keras. Implementing a CNN for regression prediction is as simple as: Removing the fully-connected softmax classifier layer typically used for classification.

** Is clustering a regression or classification **

Clustering is an example of an unsupervised learning algorithm, in contrast to regression and classification, which are both examples of supervised learning algorithms. Data may be labeled via the process of classification, while instances of similar data can be grouped together through the process of clustering.

** Why Naive Bayes is better than SVM **

The training cost of SVM for large datasets is a handicap.

Both NB and SVM allow the choice of kernel function for each and are sensitive to parameter optimization. Comparing the accuracy of SVM and NB in spam classification showed that the basic NB algorithm gave the best prediction results (97.8%).

** Is Naive Bayes a bad classifier **

Disadvantages of Naive Bayes

This algorithm is also notorious as a lousy estimator. So, you shouldn't take the probability outputs of 'predict_proba' too seriously. It assumes that all the features are independent. While it might sound great in theory, in real life, you'll hardly find a set of independent features.

** Is XGBoost a regression model **

Therefore, XGBoost is an applicable machine learning regression model that can be applied to accurately predict the Es and Ed.

** Is logistic regression a Bayes classifier **

Naive Bayes Classifier is an example of a generative classifier while Logistic Regression is an example of a discriminative classifier.

** Is logistic regression a generative classifier model **

Generative and discriminative models are widely used machine learning models. For example, Logistic Regression, Support Vector Machine and Conditional Random Fields are popular discriminative models; Naive Bayes, Bayesian Networks and Hidden Markov models are commonly used generative models.

** Are naive Bayes and linear regression the same **

The comparison with linear regression depends on the error measure: for one measure naive Bayes performs similarly, while for another it is worse. We also show that standard naive Bayes ap- plied to regression problems by discretizing the target value performs similarly badly.

** Is Bayesian a linear regression **

In the Bayesian viewpoint, we formulate linear regression using probability distributions rather than point estimates. The response, y, is not estimated as a single value, but is assumed to be drawn from a probability distribution.

** Is decision tree a regressor **

Decision trees is a type of supervised machine learning algorithm that is used by the Train Using AutoML tool and classifies or regresses the data using true or false answers to certain questions. The resulting structure, when visualized, is in the form of a tree with different types of nodes—root, internal, and leaf.