Machine Learning Tutorial Python - 15: Naive Bayes Classifier Algorithm Part 2

In this python machine learning tutorial for beginners we will build email spam classifier using naive bayes algorithm. We will use sklearn CountVectorizer to convert email text into a matrix of numbers and then use sklearn MultinomialNB classifier to train our model. The model score
with this approach comes out to be very high (around 98%). Sklearn pipeline allows us to handle pre processing transformations easily with its convenient api. In the end there is an exercise where you need to classify sklearn wine dataset using naive bayes.

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Topics that are covered in this Video:

00:00 explore spam email dataset
02:33 sklearn CountVectorizer
04:30 types of naive bayes classifiers
05:23 sklearn MultinomialNB classifier
06:48 sklearn pipeline
09:35 Exercise

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[vishalgupta]

Sir You are amazing, an experience of 25 years is really brilliant, Thanks for Guiding us

[r]

Excellent channel to start learning the ML concepts...Way better than almost all the paid courses out their

[kamalsingh]

Thanks a lot for this playlist of such amazing tutorials.
at test_size=0.2, GaussianNB: 97.2% and MultinonialNB: 77.3%

[anujvyas]

Solved the exercise, got these answers:
Using Gaussian : 1.0
Using Multinominal : 0.889

[moeintorabi]

The Guassian model is more accurate. As mentioned in the video, the Gussian model is more accurate for cases where the features have continuous values, which is the case for the Wine dataset.

[bechirmariam]

i can just say that you are a perfect teacher, Thank you very much. This is a best channel to learn all about datascience!!!

[Yash]

I got
100% accuracy with Gaussian NB
96% accuracy with Multinomial NB


Thanks for explaining in a very easy and convenient way :)

[OmkarShelke-wj]

it is the most practical and easiest to understand series for begineers

[moeintorabi]

For comparing the models I used Cross Validation (CV = 4) as you explained in the previous videos.
Average Gaussian Score =
Average Multinomial score =

[tsai]

GaussianNaiveBayes 0.972/ MultinomialNaiveBayes 0.94. MinMaxScaler train dataset. This series of tutorials are strongly recommended. Help me a lot

[austinwhite]

I think you might be the most valuable resource online for ML beginners.

Gaussian: 100%
Multinomial: 86.1%

[gajanantayde]

Thank you for this wonderful tutorial
Exercise scores
GaussianNB score - 94.5%
MultinomialNB score - 84.5%

[mehmetyigitakn]

outstanding video series! greetings from Turkey, I learn too much from this channel. It's now my primary go-to resource to learn machine learning from scratch

[navneetkaurpopli]

All your ML videos are wonderful. Good job. Difficult things explained easily. Thanks

[larrybuluma]

Gaussian: 1.0
Multinomial: 0.833
Keep up the good work you're doing

[userhandle-ub]

Thanks a lot for the tuto. Your series is best because it contains the exercises.
My exercise result: GaussianNB = 0.96, MultinomialNB = 0.84. I also applied cross validation =5

[premnathmagi]

I don't know why some people have disliked this video. How beautifully he is explaining the M.L algorithms.

[TheNobody]

My scores are : Multinomial NB = 0.84, Gaussian NB = 0.97. Thank you so much for these videos :)

[charmindesai]

Amazing tutorial, you teach far better than university professors. Following many of your playlist thoroughly !!! Thank you very much

[piyushjha]

Exercise answer:
Gaussian : 1.0
MultinomialNB : 0.889

Sir u use random state in your solution.Thank you sir i learned something new