Why Logistics Does Better Than Naive Bayes?

Logistic Regression vs Naive Bayes : Naive bayes is a generative model whereas LR is a discriminative model. Naive bayes works well with small datasets, whereas LR+regularization can achieve similar performance. LR performs better than naive bayes upon colinearity, as naive bayes expects all features to be independent.

Why does logistic regression perform better than Naive Bayes?

If the data set follows the bias then Naive Bayes will be a better classifier. Both Naive Bayes and Logistic regression are linear classifiers, Logistic Regression makes a prediction for the probability using a direct functional form where as Naive Bayes figures out how the data was generated given the results.

Is logistic regression faster than Naive Bayes?

I can’t find a reference now, but e.g. in classification, naive Bayes converges quicker but has typically a higher error than logistic regression. On small datasets you’d might want to try out naive Bayes, but as your training set size grows, you likely get better results with logistic regression.

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What is the main disadvantage of Naive Bayes?

Naive Bayes assumes that all predictors (or features) are independent, rarely happening in real life. This limits the applicability of this algorithm in real-world use cases.

Why Naive Bayes algorithm is best?

Pros: It is easy and fast to predict class of test data set. When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data. It perform well in case of categorical input variables compared to numerical variable(s).

What is better than Naive Bayes?

Naive bayes is a generative model whereas LR is a discriminative model. LR performs better than naive bayes upon colinearity, as naive bayes expects all features to be independent.

How is logistic regression difference from Naive Bayes?

Naïve Bayes is a classification method based on Bayes’ theorem that derives the probability of the given feature vector being associated with a label. Logistic regression is a linear classification method that learns the probability of a sample belonging to a certain class.

What is the benefit of Naive Bayes in machine learning?

Advantages. It is easy and fast to predict the class of the test data set. It also performs well in multi-class prediction. When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data.

What is the difference between Bayes and Naive Bayes?

Well, you need to know that the distinction between Bayes theorem and Naive Bayes is that Naive Bayes assumes conditional independence where Bayes theorem does not. This means the relationship between all input features are independent. Maybe not a great assumption, but this is is why the algorithm is called “naive”.

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Is Naive Bayes used for regression?

Naive Bayes classifier (Russell, & Norvig, 1995) is another feature-based supervised learning algorithm. It was originally intended to be used for classification tasks, but with some modifications it can be used for regression as well (Frank, Trigg, Holmes, & Witten, 2000).

What is the benefit of naive bias?

Advantages of Naive Bayes Classifier It is simple and easy to implement. It doesn’t require as much training data. It handles both continuous and discrete data. It is highly scalable with the number of predictors and data points.

What are the strengths and weaknesses of Naive Bayes algorithm?

Strengths and Weaknesses of Naive Bayes

  • Easy and quick way to predict classes, both in binary and multiclass classification problems.
  • In the cases that the independence assumption fits, the algorithm performs better compared to other classification models, even with less training data.

Is Naive Bayes difficult?

Although Naive Bayes is a very fast and simple classifier, but there is some disadvantages that may degrade its work: 1- It assumes the attributes are independent.

Why naive Bayes is naive?

Naive Bayes is called naive because it assumes that each input variable is independent. The thought behind naive Bayes classification is to try to classify the data by maximizing P(O | Ci)P(Ci) using Bayes theorem of posterior probability (where O is the Object or tuple in a dataset and “i” is an index of the class).

Why is naive Bayes fast?

Learn a Naive Bayes Model From Data Training is fast because only the probability of each class and the probability of each class given different input (x) values need to be calculated. No coefficients need to be fitted by optimization procedures.

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Why do naive Bayesian classifiers perform so well?

Naive Bayes classification is a popular choice for classification and it performs well in a number of real-world applications. Its key benefits are its simplicity, efficiency, ability to handle noisy data and for allowing multiple classes of classification3. It also doesn’t require a large amount of data to work well.

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