Can training accuracy be 100\%?

A statistical model that is complex enough (that has enough capacity) can perfectly fit to any learning dataset and obtain 100\% accuracy on it. But by fitting perfectly to the training set, it will have poor performance on new data that are not seen during training (overfitting). Hence, it’s not what interests you.

Why is my model accuracy 100?

You are getting 100\% accuracy because you are using a part of training data for testing. At the time of training, decision tree gained the knowledge about that data, and now if you give same data to predict it will give exactly same value.

How do I know if my naive Bayes is accurate?

Naive Bayes classifier calculates the probability of an event in the following steps:

1. Step 1: Calculate the prior probability for given class labels.
2. Step 2: Find Likelihood probability with each attribute for each class.
3. Step 3: Put these value in Bayes Formula and calculate posterior probability.

Is Overfitting 100 accurate?

High validation scores like accuracy generally mean that you are not overfitting, however it should lead to caution and may indicate something went wrong. It could also mean that the problem is not too difficult and that your model truly performs well.

How do I make naive Bayes more accurate?

Better Naive Bayes: 12 Tips To Get The Most From The Naive Bayes Algorithm

1. Missing Data. Naive Bayes can handle missing data.
2. Use Log Probabilities.
3. Use Other Distributions.
4. Use Probabilities For Feature Selection.
5. Segment The Data.
6. Re-compute Probabilities.
7. Use as a Generative Model.
8. Remove Redundant Features.

How do you improve classifier accuracy?

8 Methods to Boost the Accuracy of a Model

1. Add more data. Having more data is always a good idea.
2. Treat missing and Outlier values.
3. Feature Engineering.
4. Feature Selection.
5. Multiple algorithms.
6. Algorithm Tuning.
7. Ensemble methods.

Is 99 accuracy good enough?

And that, is highly dependent on the reliability of the data you’re starting with. If you’re looking for 1 in 1,000, then 99\% accuracy isn’t good enough, because 90\% of your results will be wrong.

What is a good classification accuracy?

Therefore, most practitioners develop an intuition that large accuracy score (or conversely small error rate scores) are good, and values above 90 percent are great. Achieving 90 percent classification accuracy, or even 99 percent classification accuracy, may be trivial on an imbalanced classification problem.

Is 80\% accuracy good for a model?

If your ‘X’ value is between 70\% and 80\%, you’ve got a good model. If your ‘X’ value is between 80\% and 90\%, you have an excellent model. If your ‘X’ value is between 90\% and 100\%, it’s a probably an overfitting case.

What is naive Bayes algorithm in machine learning?

The name naive is used because it assumes the features that go into the model is independent of each other. That is changing the value of one feature, does not directly influence or change the value of any of the other features used in the algorithm. Alright. By the sounds of it, Naive Bayes does seem to be a simple yet powerful algorithm.

What is the Bayes rule in machine learning?

The Bayes Rule is a way of going from P(X|Y), known from the training dataset, to find P(Y|X). To do this, we replace A and B in the above formula, with the feature X and response Y. For observations in test or scoring data, the X would be known while Y is unknown.

Can my data be perfectly classifiable by a linear classifier?

No, your data may not be perfectly classifiable especially by a linear classifier and this is not always because of the classifier or the features you are using. None of the features may contain sufficent differences to provide a clear line.

What is the naive Bayes rule?

When the features are independent, we can extend the Bayes Rule to what is called Naive Bayes. It is called ‘Naive’ because of the naive assumption that the X’s are independent of each other. Regardless of its name, it’s a powerful formula. In technical jargon, the left-hand-side…