Training Data Vs Test Data Vs Validation Data| Krish Naik

github for materials and notes: github.com/kri...
Training set: A set of examples used for learning, that is to fit the parameters of the classifier.
Validation set: A set of examples used to tune the parameters of a classifier, for example to choose the number of hidden units in a neural network.
Test set: A set of examples used only to assess the performance of a fully-specified classifier.
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[@netviz8673]

when data set is given to create a model then train dataset is on which the model is trained and it usually uses 70-80% of the data set. Test data uses 20% of the data set and is used for model performance analysis, prediction is made and performance metrics is checked.
Now validation data is used for model hyperparameter tuning. We take the data slightly from the training data set. Using cross validation the validation data is obtained from training data set. CV=5 means 5 experiments. If total training data is 8000 then 8000/5 = 1600 means for every cross validation the starting 1600 data points will be used as validation data and the rest of 6400 data points will be used as train data and the accuracy is obtained for the first experiment. In second experiment when cross validation is 2 then then next 1600 data points will be used as validation data and first 1600data points + next 4800 data points = 6400 data points will be used for train data set and so on. So with every exp the validation data and train data set vary a little.

[@hunterworrier6769]

Krish bro... U should provide simple code of this all video too

[@zaafirc369]

Thank you
Clearly explained as always !
Is it possible to implement an example of cross validation and hyper parameter tuning on python pls? 🙏

[@MuhammadJunaid-yr8jd]

Perfectly explained sir understood very well
Thanks a lot sir

[@sukruthraghavendra7305]

Perfectly explained sir understood very well

[@anjalipes]

Thank you for the lovely explanation sir.
I have a doubt in the validation data, once you have taken the mean or Avg of the CV, what do we do next with that mean? how does it help in the performance of your model? Does this impact testing data?

[@sketchytv1321]

خوش رہو بہت اعلیٰ کام کر رہے ہیں ۔

[@riya-tb6sj]

Hello sir i m 12 Ai frst tym i watched ur video and frst tym understood concept plz make more videos of 12 syllabus cbse

[@milansood8240]

thank you sir,
I'm enrolled in upgrad Data Science program and not satisfied with it as the content is not much special like yours.

[@iamalien8327]

sir thanks for creating the videos
sir kindly guide which book to refer for making the notes

[@murthy33]

Please upload English videos. It is very helpful

[@bivasbisht1244]

Why hyperparameter tuning is done after training the model ? Shouldn’t it be done before traning the model , and then train the model based on those parameters ?

[@fran9426]

You do both. Once you have done hyperparameter tuning you can then train the final model with the tunes values on all the training data

[@musicallystatus5368]

@9:52 why you divide for cross velidation= 5

[@muazamazam7115]

Thank you for making this piece of sweet pie ❤️🌹

[@mahantachauhan4461]

Thanks for the such an informative video 👏👏

[@123vijit]

What is hyperparameter?

[@richadhiman3300]

Beautifully explained sir...

[@jyotikapatil2354]

This was cross-validation. How hyperparameter tuning is done?

[@zaafirc369]

ROC AUC explanation also pls

[@omdodmani3205]

if test data is used for accuracy , then which accuracy is shown by the cross validation method ?

[@rds9815]

Hi krish
Please let me know what is x_train ,y_train and x_test ,y_test and how this process works on the data set including the mathematical equation I have a lot of confusion on this. Please make a separate video on this Both languages of English and Hindi

[@sumitlasiwa7152]

train and test data is further divided into independent and dependent features. x-train is for independent and y-train for dependent of train dataset. This is done to let model know the difference between independent and dependent features. Similarly, same thing for test dataset. In test dataset this is done to compare the model prediction and actual value(y-test) to know the accuracy.

[@rds9815]

@@sumitlasiwa7152 Thank you doubt cleared

[@jaiprakashsingh756]

sir, could you please share some notes so that we can revice all the bulletin points.

[@divyadewangan6123]

you are the best person to explain any topic .Thanx🥰

[@theunscripted643]

Ur so amazing sir ❤

[@neal_indian555]

Sir you are great Akshay Kumar is not Boss You are the BOSS

[@AlAmin-xy5ff]

excellent

[@rohitjana7461]

Thank you sir ☺

[@akshaymondal1372]

Sir also make a video on data warehouse data lakes

[@maths_impact]

Outstanding sir

[@vasimshaikh9857]

Thank you sir 💖💖💖💖💖

[@PS-rh8vy]

Superb ❤

[@vaibhvkushwah]

kafi kya bahut jyda acha tha

[@hunterworrier6769]

But very helpful

[@neal_indian555]

Hukum ka ekka BOSS

[@swagatsanketpriyadarsan6231]

I just gave the 1000th like. :P

[@abhinavrajsaxena789]

6400

[@AdityaSharma-yc8yt]

Didn't liked it !

[@vibhu613]

[@TheNeu2ron]