Difference Between fit(), transform(), fit_transform() and predict() methods in Scikit-Learn
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- Опубликовано: 8 фев 2025
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I always get confused about fit() ,transform(), fit_transform()....thank you sir... you are like a saviour to many people like me...
Still not got
@@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.
@@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.
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!
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
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.
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 ( ).
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 ????
it is very useful video krish, now i got a clear information about fit and transform thanks giving this useful information krish .
Batman, Superman and Krish Naik
So clear explanation that i can also understand process of machine learning. Thanks a lot
Thank you Krish; it is just another beautiful video of your very helpful videos
Krish, you are a LEGEND!!!!!!!!!!!! Thanks much for making these enlightening tutorials!!!!!!!
I was not clear with, why fit_transform for train data and only transform for test. Now i understood this concept.
Thank you!!!
fit -> trains the underlying model (or) calculates parameters for a model based on the train subset
you only perform those operations on the train subset and not the test subset.
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!
Krish sir , I got lots of idea on fit(), transform(), fit_transform() and predict() methods. Thanks a lot.
awesome video thanks very much. big shout out to all indians out there helping out the world. big greetings from brazil
old videos are gold, thanks for this krish
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
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 ❤
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!!!!!!!!!!!!!!!!!!!!!
Beautifully Explained
Just in one sort u cleared all doubts. Thanks 👍
Hats off sir!! Your explanation is of God level 💯 Thank you sir ❤️
God bless you!!! Your videos make everything simple.
Thank you soo much ,, was struggling to understand this concept .superrr well explained
Crystal clear and detailed . Awesome. Keep it up
This is clear, in-depth, comprehensive, and helpful! Thank you so much!
Very informative. Well explained. Thank you .
it amazing video i had come through a great understanding and very easy to understand the concept thank you sir
I love you man you are a game changer god bless you please load more videos
Finally😁..Thanks for uploading
thank you sir ..I always get confused but now its clear. Thank you soooo much
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
Thanks a lot you have contributed a lot to this community
Thanks for this
Superbbb explanation brother...
Excellent Explanation !!!!
THANK YOU KRISH AMAZINGGGG BLESSINGS TO YOU
Sir you explain so good .Thankyou for this
Nice 👍
Great Video
Awesome explanation :)
It's amazing👍
Hello, you're such a good teacher! This helped me a lot. Thank you!
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
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.
@@kavanadeshpande9690 Thanks, great information. That give me the right way to go ahead.
Please have a nice day :)
Laurent
@@kavanadeshpande9690 Hi, thanks a lot for your answer.. I understand better now :)
Please have a nice day
Laurent
Can you please make video on different types of transformation viz standardscaler, minmaxscaler etc and when to use which
hi krish, can you make a full video of how to do deployment full process video, including all steps.
Simple and straightforward! Thanks!!👏
thank you for your tutorial. There's one serious issue that I want to address here. As far as I know, we're not allowed to do anything that results in leakage from test data to train data. So when you do a fit_transform on a train_data and save the parameters in the scaler, it's okay to do scaling on the test data based on that very scaler, but not the other way around!! Because there would be a leakage for mean and s.d from train data to test. This way always the result would be better but it's because of the cheat that is happening and the model really. So be careful with the order of steps you go through when scaling train and test data.
I too feel the same...we have to fit and transform on the test data also..to avoid data leakage
what if we fit on whole data and then split and transform train and test data. This way test data will not depend on training parameters. also no data leakage will occur
thanks so much for the value of your videos 💯💯
Again, Thank you Krish, well explained.
Its very helpful video sir
Thanks for guiding
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.
you have cleared my concept
Hi Krish
It would be really helpful if you create a playlist on tensorflow serving and tensorflow lite.
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.
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.
Thank You, understood
Clearly Explained ! Thanks a lot !!!
Great explanation and intuition (Y)
Excellent!
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?
Best video! One question: Where is y_test used?
amazing explanation, thx bro
Thank you so much for such a brilliant explanation!
Thank you so much, sir for this lecture.
thank you for clearing my doubts sir
Well Explained. Really very informative. Thankyou so much :)
Thanks Krish
Classifier algorithm whose using distance usually do normalize the datasets before put to model
superb
Thank you so much, this helped me a lot :)
Thank you so much for all of the valuable content you shared!
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,
Thank you Krish
Can we apply the transformation for the y(independent variables) value also or should it be applied only to X(dependent variables)
So where should you use fit(), transform(), fit_transform() during a K-Fold Cross Validation? Before CV or During CV?
You are amazing !
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.
Hi Krish please make a video on difference between map(), flat_map() and apply() in tf.Dataset
very helpful!! Thanks!!
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?
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
nice sir
Thank you very much sir🙏
this is beautiful
Thanks sir.... First view👍
Can you state the screen recording software and the settings you have used for this recording? Thank you.
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
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
Thank you so much...
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
same doubt
what is the writing pad you use ?
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."
In which platform did you tell this lesson? you can use your pencil properly.
Sir unable to access your github filescode IAM learning python from 12 April 10:00am
Thanks
If the train data and test data unique values are different then how can we apply label encoder with fit and transform?
Where to put outlier detection in ur data processing chain ??
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
Thank you .
Plz make video on image recognition in jupyter note book and deployment technique with deep explanation
Thank you sir
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
15:34 amazing
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