GaussianNB | Gaussian Naive Bayes classifier | Machine Learning library
kandi X-RAY | GaussianNB Summary
kandi X-RAY | GaussianNB Summary
Gaussian Naive Bayes (GaussianNB) classifier. Simple Gaussian Naive Bayes classifier implementation. It also implements 5-fold cross-validation. Compared performance with Zero-R algorithm.
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Top functions reviewed by kandi - BETA
- Calculates the prediction for each test set
- Calculate the probability of x
- Splits data into training and test sets
- Calculate accuracy
- Prints information about the user
- Provide the most frequent prediction
GaussianNB Key Features
GaussianNB Examples and Code Snippets
Community Discussions
Trending Discussions on GaussianNB
QUESTION
I am using a multiclass classification-ready dataset with 14 continuous variables and classes from 1 to 10. This is the data file: https://drive.google.com/file/d/1nPrE7UYR8fbTxWSuqKPJmJOYG3CGN5y9/view?usp=sharing
My goal is to apply the scikit-learn Gaussian NB model to the data, but in a binary classification task where only class 2 is the positive label and the remainder of the classes are all negatives. For that, I did the following code:
...ANSWER
Answered 2022-Mar-20 at 13:08Your code looks fine; this is a classic problem with imbalanced datasets, and it actually means you do not have enough training data to correctly classify the rare positive class.
The only thing you could improve in the given code is to set stratify=y_d in train_test_split, in order to get a stratified training set; decreasing the size of the test set (i.e. leaving more samples for training) may also help:
QUESTION
I am experimenting in sklearn learn classification with some NLTK type tutorials. Can someone help me understand why the sklearn MLP neural network can handle different input shapes but the other classifiers cannot?
My input training data is a numpy.ndarray with shape (62, 2)
This is the only thing I know how to do for train test split (any tips appreciated if there is something better)
...ANSWER
Answered 2022-Jan-10 at 14:14As far as I can see the labels y are in the one-hot form. Basically label is a vector with size equal to the number of classes. Each element of that vector is zero except at the index which represents the exact class. That element is one. This is why the shape of y is (62, 15)
U need to transform the label y into a form in which your labels will be represented as integers.
Example:
In this example we have 6 classes: ranging from 0 to 5
[0, 0, 0, 1, 0, 0] -> 3
[1, 0, 0, 0, 0, 0] -> 0
[0, 1, 0, 0, 0, 0] -> 1
You can do this by using the numpy.argmax(y, axis=1) which will return the index of element which has maximum value along the specified axis. Take a look at the documentation
QUESTION
I'm trying to calculate some metrics using StratifiedKFold cross validation.
ANSWER
Answered 2022-Jan-04 at 07:01Import NumPy and Use this:
QUESTION
I trained an algorithm to make weather prediction on a test set.
...ANSWER
Answered 2021-Dec-08 at 01:31If you want to ignore them, why would you not remove them from the training/test set? I mean, removing them will make you loose information, but do you need that information? Maybe you can take a look at some imputation methods explained here. In case that you want to replace NaN with specific vlaue, you can use for instance:
QUESTION
I have the following code, where I predict if it is going to rain the next day. As result I get 1 or 0.
...ANSWER
Answered 2021-Dec-07 at 23:02If your shape is (1, 11540) and you want one value per row, transpose your array.
QUESTION
I am working on an ID3 algorithm implementation. The issue that I am running into is processing the branches from the new root attribute
As the print shows
...ANSWER
Answered 2021-Nov-20 at 20:46This seems to do it.
QUESTION
I have 2 nominal values of a product (category and producer) and its price and try to identify if in any given category a producer has usually a higher price. In another word, I try to measure the impact of a brand on price. I used below Python code and couldn't manage to run and get this error:
...ANSWER
Answered 2021-Nov-04 at 08:48You get that error because your dependent variable is continuous and you are trying to do a stratified kfold which doesn't make sense
If it is like you said:
In another word, I try to measure the impact of a brand on price.
Then your dependent variable should be price. Your independent variables would be the nominal values. And you should do one-hot encoding instead of label encoding because these are not predictors, not labels.
Using an example dataset:
QUESTION
I am going to put a very easy example of what I'm trying to do before posting my code
example 1: This is what I want to accomplish(but with the keywords inside a list)
...ANSWER
Answered 2021-Sep-24 at 16:58You can't define a variable in a list. Here's how you could make this function work (assuming I read the question right)
QUESTION
I am currently doing "Intro to Machine Learning" free course in udacity, where there's a quiz regarding Gaussian Naive Bayes. The code is giving desired output when running in the udacity environment(as shown in image below) Code output in udacity environment
But it is showing error when I run it in jupyter notebook, for module class_vis.py it's showing error 'NoneType' object has no attribute 'predict'(as shown in image below) Error in jupyter notebook
Here are the codes for all modules :-
- studentMain.py
ANSWER
Answered 2021-Sep-20 at 16:55As far as I see your classify function doesn't return anything, yet you assign it's return value to a variable which will set it to None according to python standards. To fix this insert a return statement at the classify function:
QUESTION
I am using cross_validate from Sklearn and it is working fine for multiple models such as GaussianNB, RandomForestClassifier, KNeighborsClassifier, GradientBoostingClassifier and XGBClassifier but when using it with SVC it returns nan. Here's my code and the things I've tried.
ANSWER
Answered 2021-Aug-24 at 21:42For anyone facing the same issue you need to enable probability=True in the model.
It seems that Sklearn carries any error for all the scores. Probability is needed for roc_auc_ovr_weighted
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Install GaussianNB
You can use GaussianNB like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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