Difference Between fit(), transform(), fit_transform() and predict() methods in Scikit-Learn

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[@jammerules80]

Thank you for the clear explanation. I spent $10K learn ML-AI from UC Berkeley and yet I could not understand this concept before this video. Job well done!

[@survivor9367]

Actually I am searching for this in on other videos. As it is not available in you lr play list.. You just updated.. Thank you so much sir

[@alessandrodf888]

Batman, Superman and Krish Naik

[@nguyenthituyetnhung1780]

So clear explanation that i can also understand process of machine learning. Thanks a lot

[@pranaypakhale]

Can you please make video on different types of transformation viz standardscaler, minmaxscaler etc and when to use which

[@theforester_]

awesome video thanks very much. big shout out to all indians out there helping out the world. big greetings from brazil

[@nigamaveena4211]

I always get confused about fit() ,transform(), fit_transform()....thank you sir... you are like a saviour to many people like me...

[@cocgamingstar6990]

Still not got

[@pratikghute2343]

@@cocgamingstar6990 see bro, first of all we use .fit in two scenarios first one is at time of scaling and second one is at models training. (scaler.fit_transform(xtrain) and scaler.transform(xtest) that is part of Data preprocessing step and the second scenario we use .fit is at model training (model.fit(xtrain)) there we use fit to fetch the parameters like slope and y intercept.

[@TheKumarAshwin]

@@cocgamingstar6990 It happens here is in summery, fit(): Learns from data (training). transform(): Applies the learned transformation to data. fit_transform(): Combines fit() and transform() in one step.

[@aishwaryanarkar2954]

THANK YOU KRISH AMAZINGGGG BLESSINGS TO YOU

[@adipurnomo5683]

Classifier algorithm whose using distance usually do normalize the datasets before put to model

[@hanscesa5678]

So where should you use fit(), transform(), fit_transform() during a K-Fold Cross Validation? Before CV or During CV?

[@priyadarshanr296]

Sir , I can understand that it formats the test data in the same format of train_data , but how does transform function helps to overcome overfitting,

[@shashikarathnasinghe1241]

Thank you soo much ,, was struggling to understand this concept .superrr well explained

[@sugavananv]

Best video! One question: Where is y_test used?

[@shaelanderchauhan1963]

Thanks a lot you have contributed a lot to this community

[@vineetj9102]

Can we apply the transformation for the y(independent variables) value also or should it be applied only to X(dependent variables)

[@kiranvanukuri9382]

Plz make video on image recognition in jupyter note book and deployment technique with deep explanation

[@rishabhkumar-qs3jb]

Awesome explanation :)

[@KiranGunda-ph7df]

Superbbb explanation brother...

[@manishaundale7458]

If the train data and test data unique values are different then how can we apply label encoder with fit and transform?

[@darshanayenkar]

you have cleared my concept

[@SUMITKUMAR-qi6mz]

I am having experience of 1 year in customer service in BPO but I want toh become Data scientist . But I'm having difficult toh get job in same because they are asking for experience in data science. Pls help me how to portrait my resume to get job

[@merveozdas1193]

In which platform did you tell this lesson? you can use your pencil properly.

[@arshad1781]

Nice 👍

[@optimusagha3553]

Simple and straightforward! Thanks!!👏

[@shadmanansari5750]

Hi, You mentioned that Fit_transform() is applied on Training data and only Transform() is applied on Test data, So, in case of StandardScaler, Fit_transform(Train) will have mean and std dev of train data, and then we are using same mean and std dev on 'Test data' Should'nt we apply Fit(on entire data) to calculate mean and standard dev of entire data, then transform(train) and transform(test)? Please clarify

[@ece15amritanshusingh22]

same doubt

[@anirbanc88]

superb

[@munawarabbasi9683]

Thanks for making it a complete halwa.

[@jlxip]

Thank you so much, this helped me a lot :)

[@kumarprince5054]

Thanks

[@rkkcode]

Thank you .

[@sanyamsharma3940]

You are amazing !

[@nagamohan1412]

Hi krish, I am Naga Mohan. I want to use data science or data analyst technology for my fathers agriculture land but I don't how to start actually I am so much confused. I have no data. I don't know how to create my own data for my farm land. Can you please give me tips. How to start the project and how to create the data. We have 2 acres of paddy land and 2 acres of banana land

[@milliesadie486]

thank you

[@stevetrabajo4065]

15:34 amazing

[@harshagrawal5613]

100℅ clear

[@tharallaanil4115]

sir please share me github link this program

[@mariuszwiesiolek9340]

This was fantastic, I really got the essence of not only when, how, and why to use fit(), transform(), fit_transform(), predict() but in the context I was looking for!

[@devinpython5555]

summary of this is (intuition) for train data: fit creates formula for all the features in dataset ,transform will transform data with created formula. for test data: formula already created just transform it accordingly.

[@srujankumar637]

But on test data fit( ) has not applies then how it gives transform( ) value,,, Because mean(mu) and st.dev has to Calculated for test data by using fit ( ).

[@srujankumar637]

Or data distribution is almost same for both train and test data so that's why mean and st.dev is same for train and test data... And once we got values for train by using fit () that will be transformed for test data ????

[@subhashvarma4551]

sir, if we apply the same mean in transforming the test data as in train data, this may be the case of data leakage where we are leaking information of train to test. which might not be preferable in the real-time scenario as future data should be totally anonymous to the train data. we should also perform a fit transform on the test data in such cases. Need your thoughts on this.

[@mouleshm210]

No bro, we should be cautious only on the data leakage from test to train data where, future data parameters like mean or min/max values must not be leaked while doing preprocessing, thats why we do only transform() in test data.

[@naiduvinay4911]

Thank You, understood

[@saurabhbarasiya4721]

Thanks for this

[@jamalhasanzakarneh9837]

Thank you Krish; it is just another beautiful video of your very helpful videos

[@frankdearr2772]

Hi, I understood about well what you told, but could you tell me WHY y_train is not scaled like X_train ??? For me that is because values are like false or true , if the y_train values were different like 10, 5 , 41, 5.8, etc , I think I will have to scale y_train ?? Please show me the way for that small question about your video :)) Thanks for your great video about that topic Laurent

[@kavanadeshpande9690]

Hi, as per my knowledge, scaling of dependent feature is not necessary when we have less cardinality for classification problem. For regression, if we scale the dependent feature then automatically Mean Square Error will also get scaled.

[@frankdearr2772]

@@kavanadeshpande9690 Thanks, great information. That give me the right way to go ahead. Please have a nice day :) Laurent

[@frankdearr2772]

@@kavanadeshpande9690 Hi, thanks a lot for your answer.. I understand better now :) Please have a nice day Laurent

[@MahmouudTolba]

شرحك عالي يحشوم

[@gomathic9557]

it is very useful video krish, now i got a clear information about fit and transform thanks giving this useful information krish .

[@shivu.sonwane4429]

Fit_transform use on training data but transform only on testing /new data Applies the same transformation to both set of data which creates consistent column and prevent data leakage it means learning something from testing data this is not allowed

[@bhargavikoti4208]

Finally😁..Thanks for uploading

[@oyesinghji7910]

hi krish, can you make a full video of how to do deployment full process video, including all steps.

[@parikshithh4991]

Beautifully Explained

[@hinaaqil1774]

Lets take the standardscaler formula . Its z=(x-mew)/n. .fit calculates the parameters in the formula just. here mew will be calculated only. but it doesnt change the values to new scaled valued. Now For training data We do both fit_transform It will calculate the 'mew' plus will transform the data to new scaled data. For testing As fit already calculated 'mew' for training data above , no need to calculate separate mew for test set. Just transform, it will automatically use the mew of training data and will transform to new scaled values. The same formula/parameter values needs to be applied to the test data which is calculated in training data when we did fit_transform. This will save us from overfitting.

[@ajaykuruba1738]

Hi Krish It would be really helpful if you create a playlist on tensorflow serving and tensorflow lite.

[@prekshamishra9750]

Second😀

[@prekshamishra9750]

Girls are more active here❤️...amazing explanatn krish...kudos to u guys👍👍

[@Md.SouravSarker]

Very informative. Well explained. Thank you .

[@dhivakarsomasundaram21]

o to evaluate test data we should not use fit_transform. ....... transform only requires??

[@yashub9580]

sir can you please tell me how to resolve this error "Deprecated distribution is specified in `adstock__tv_pipe__carryover__strength` of param_distributions. Rejecting this because it may cause unexpected behavior. Please use new distributions such as FloatDistribution etc."

[@arpankhadka8671]

You said for test data we only do transform, we don't do fit. But can we do transformation without fit?? For standardization mean and SD is calculated by fit according to what i understood from your video. Please explain it.

[@zaindeen4490]

Thank you so much krish sir. It was quite informative! I was searching for this kind of video but wasn't able to find it Thanks for all of your great efforts ❤

[@nellitharun8466]

Sir unable to access your github filescode IAM learning python from 12 April 10:00am

[@soukainahanafi1685]

I understand that but with a polynomial model we use fit_transform and not only fit .It' hard to understand . ##this is the example that I'm working on pr = PolynomialFeatures(degree=5) x_train_pr = pr.fit_transform(x_train[['horsepower']]) x_test_pr = pr.fit_transform(x_test[['horsepower']])

[@Trendz-w5d]

Why only transform only xtest not ytest and fit transform only xtrain not ytrain. Pls help on this. Why not on y train and ytest

[@khaboninamasemola1970]

You saved my backside with this video. Thank you.

[@Harshpatel-uw2dw]

it amazing video i had come through a great understanding and very easy to understand the concept thank you sir

[@sherin7444]

Before calling train test split why we didn't scale our data?

[@akashgautam1909]

I still don't understand why we don't perform fit on test data?

[@RAHUDAS]

Where to put outlier detection in ur data processing chain ??

[@akashkumar-bq7cl]

hi krish ,what will happen if i apply fit_transform to my test data as well?what will be the outcome?why shudnt we do it?is it because new mean and sd will be calculated for the test data?but we need the same mean and sd and formula of the train data to be applied to the test data aswellright?is that the reason we use only transform?just did not get this part and the rest of the video im so happy that so much content in just half an hour that too for free,GOD BLESS YOU PLEASE HELP

[@bluejadoo6912]

thank you for clearing my doubts sir

[@suvamgupta2914]

Hats off sir!! Your explanation is of God level 💯 Thank you sir ❤️

[@arpandas5974]

Actually I am searching for this in on other videos. As it is not available in you lr play list.. You just updated.. Thank you so much sir

[@paulkang2806]

if you are fitting, and transforming for the scalers and normalization, and you fitted (mean, stdev) for the training data, and say if you are applying it to the test data, isn't that something related with data leakage?

[@adipurnomo5683]

13:46 sir, what the real world application when we don't use test data instead we use unseen data. Is the data from unseen data need to be normalize before put into model?

[@bkpusprajkumar8744]

Thank you so much, sir for this lecture.

[@BytemeMaybe]

amazing explanation, thx bro

[@AromonChannel]

Thank you so much krish naik! i've been trying to understand this and you explain it in the very easy way, so we can easily understand it, thank you!!!!!!!!!!!!!!!!!!!!!

[@ajayjaadu42]

Sir you explain so good .Thankyou for this

[@heliyahasani6859]

I love you man you are a game changer god bless you please load more videos

[@pragavipul1563]

what is the writing pad you use ?

[@adityasharma5876]

Hi Krish please make a video on difference between map(), flat_map() and apply() in tf.Dataset

[@harikrishna-harrypth]

Krish, you are a LEGEND!!!!!!!!!!!! Thanks much for making these enlightening tutorials!!!!!!!

[@RobertoTexis-h5i]

15:28 here is what we all came for

[@pavanviswanadhapalli3512]

compared to all other channels ], your classes are so detail and very understandable, so sir please can you make a complete vedio on pca...? please sir

[@notmimul]

God bless you!!! Your videos make everything simple.

[@abdulrahiman8111]

Hi Krish. Your video was really informative and helped me understand the requirement as well as the difference between fit(), transform(), fit_transform() very well. Thank you

[@mangkhongsai9029]

Thank you so much...

[@louerleseigneur4532]

Thanks Krish

[@superfreiheit1]

I cant read this text

[@tonnysaha7676]

Thank you very much sir🙏

[@1111Shahad]

Thank you Krish

[@robertoespinoza199]

thanks so much for the value of your videos 💯💯

[@subhamsaha2235]

Sir, you didnt tell one thing is that if we are applying fit and transform to X_train which means (for standard scalar) fit(calculating mu and sigma) then transform(applying z formula to every value), and ONLY transform to X_test which means mu and sigma are not calculated then how is it transforming the values? I think something else is also there in fit which is used to teach the model? Kindly clear my doubt. Thank you

[@saikiranreddykondapalli279]

while transforming test data we are using actually the mue and sigma values of trained data and comparing the transformed test data with predicted data .(this is what he actually mean).but it is wrong to do we cant use mue and sigma values of other data.so it is always better to split only after all the data set is fit and transformed.the it is quite valid to check predicted and actual test values

[@shivamshinde9810]

very helpful!! Thanks!!

[@tatendaVIDZ90]

this is beautiful

[@hiral9591]

It's amazing👍

[@Trendz-w5d]

Thank you sir

[@soajack]

Clearly Explained ! Thanks a lot !!!

[@Fatih9837]

Great Video

[@vlogsbybushra]

EDA?stands for?

[@ishanagrawal396]

Exploratory data analysis

[@naeymaislamph.d9976]

Excellent!

[@bivasbisht1244]

amazing

[@eitanshirman9072]

Thank you so much for such a brilliant explanation!

[@muhammadzeerakkhan6300]

Great explanation and intuition (Y)