Man, u save my life ty very much. Use sklearn is too easy, justify why u decide to use Naive and why u can use it is the very important thing, keep it up man . ( excuse me for my bad english )
I was importing a mysql dataframe, I was importing string elements and it resolved them into objects, data = pd.read_sql_table("ai_learning", engine) columns_to_convert = ["Products", "feedback", "blog", "diagnosis"] data[columns_to_convert] = data[columns_to_convert].apply(pd.to_numeric, errors='coerce') data = data[["Products", "feedback", "blog", "diagnosis"]] This is how I fixed it if anybody was getting the same outputs.
When you are checking for gaussian curve, shouldn't you have filtered for different diagnosis and then check if the curve fits? Because now, we see the data fits gaussian. But we then change the data and only take a subset and then fitting the curve Thanks for the great video.
Hi, I am getting error as "index 29 is out of bounds for axis 0 with size 29" for this statement likelihood[j] *= cal_gaussianLikelihood(df,features[i],x[i],Y,labels[j]), any solution?
likelihood = [1] * len(labels), post_prob = [1] * len(labels) what this above code actually do? And also how can I work this code on tennis.csv dataset?
That's just a python shortcut. arr = [1] * n this will assign a list of n 1's to the variable 'arr'. I used this to initialize the likelihood and posterior probability lists. The tennis dataset contains only categorical variables so just relabel them to 0,1,2, etc. and apply approach 2 given the video.
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The from scratch series in this channel is the best !!
Thanks bro!
I can't believe this man is doing this for free, thank you brother
the number of subscribers to your channel does not do justice to your content. This is such quality educational content. Keep it up, man.
Thanks man,your effort to make algorithms from scratch is just on another level.Your effort is much appreciated👍
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Super high quality videos! I'm surprised you have 8K and not 800K... Keep it up!
Thanks a lot! Keep supporting :D :D
Man, u save my life ty very much.
Use sklearn is too easy, justify why u decide to use Naive and why u can use it is the very important thing, keep it up man .
( excuse me for my bad english )
You're a legend my dude, thanks so much for explaining this
Thank you for opening up new horizons for me
this gave a lot of clarity , thanks
Excellent video. Keep up the good work 🙂
Thank you! Will do!
Fantastic video, very well explained!
You can also ignore mean_radius feature, since it has some correlation with other features
@normalized Nerd How do you make a prediction with this using specific values?
SVMs,Random Forest and gradient boosting left in the playlist
Humm...I'll add them eventually.
I was importing a mysql dataframe, I was importing string elements and it resolved them into objects,
data = pd.read_sql_table("ai_learning", engine)
columns_to_convert = ["Products", "feedback", "blog", "diagnosis"]
data[columns_to_convert] = data[columns_to_convert].apply(pd.to_numeric, errors='coerce')
data = data[["Products", "feedback", "blog", "diagnosis"]]
This is how I fixed it if anybody was getting the same outputs.
Very nice! , I think the only thing missing is smoothing in case you encounter a wild case?!
Sorry, again I do understand now, and also I apply in my work with excellent results, Thanks!
Great to hear that!
What 36 and 74 means at 13:49?
Great lesson. But still don't understand how we get array with posterior given certain set of features befor using np.argmax?
Damn, I was hoping for a SKlearn tutorial!
opps
amazing content and fantastic explanations
:D
When you are checking for gaussian curve, shouldn't you have filtered for different diagnosis and then check if the curve fits?
Because now, we see the data fits gaussian. But we then change the data and only take a subset and then fitting the curve
Thanks for the great video.
😃Bro thx for the nice explanation. Are you using a theme for vs code, cuz all the colours in your systems are looking damn good
atom material theme
Hi, I am getting error as "index 29 is out of bounds for axis 0 with size 29" for this statement likelihood[j] *= cal_gaussianLikelihood(df,features[i],x[i],Y,labels[j]), any solution?
Awesome video.
likelihood = [1] * len(labels), post_prob = [1] * len(labels)
what this above code actually do?
And also how can I work this code on tennis.csv dataset?
bhai iska explanation bta do.
That's just a python shortcut.
arr = [1] * n
this will assign a list of n 1's to the variable 'arr'. I used this to initialize the likelihood and posterior probability lists.
The tennis dataset contains only categorical variables so just relabel them to 0,1,2, etc. and apply approach 2 given the video.
@@NormalizedNerd bhai can you tell me what is the name of the python shortcut? and how likelihood = [1] * len(labels), replaced with likelihood[b].?
Hello! Is it possible to add the multinomial in the code? Thank you.
I did not understand the output, we were detecting the cancer patient, but in out put there are two matrix and accuracy data so which is which.
Nice video
Thanks!
perfect!
Sorry, but I do not understand who is "df" when you def a function because you have never defined. I will appreciate your explanation
DataFrame, just means the data
Bro for this code how to convert string to float bro
can someone explain me the guassian distribution part