Explained so well. My confidence in DS increases day by day through your videos. 😊

great work, very clear and helpful for my project that I am working on. Thanks a lot!

Let me tell u you are the gem of the game out of all the ones teaching data science on KZread passing us real world thought process of a datascientist working in a real world scenarios Love from Karachi Pakistan,🇵🇰

A TRUE LEGEND AND MASTER OF DATA SCIENCE!!!! THANK YOU KRISH NAIK!!! YOU'RE A REAL GEM FOR THE WORLD OF DATA SCIENCE!!!!! GOD BLESS YOU MAN! ✌️💖

@jesuskristus18

2 жыл бұрын

Great, another Indian/Pakistani “data scientist” from Fiverr.

Thanks again Krish Naik amazing efforts and commitment so grateful.

I love ur clarity on the subject . Best teacher in the youtube

Damn!!! I couldn't thank you enough for this ever.. 🙏🏻🙏🏻

Thankyou so much sir for this detailed explanation ❤️

Thanks for sharing the knowledge.

U just covered all the stuff in one cool video, this just blew my mind bro. I just cant say one reason for not subscribing your channel. Thank you very much...! 💕

Krish really amazing knowledge sharing ..gr8 work..

THANK YOU BRO IT WAS AMAZING SESSION

BEST VIDEO EVER .....HATS OF TO YOU SIR 🙏

Thank you sir 🌟

Hi Krishna I have seen your most of the video

Thank you so much sir 😊😊

Sir please make a video on yolo object detection 🙏

please do a video on FasterRCNN and Yolo object detection

if possible.. please do video on faster rcnn and yolo object detection without github repo.. or even with github repo.

first time I ma seeing you in funny mood, good to see you like this else aap to bhagwan shanker ki tere gusse mein hi dikhte hain.

Hello sir, I am from Bangladesh and always watch your video. Can you make some videos about fusion models.

Sir Please makes video on Mathematics behind on SVM Regression, AdaBoost Regression, Gradient Boost Classification

finished watching

9:30 GridSearchCV and RandomizedSearchCV are good

WHEN WILL OBJECT DETECTION GOING LIVE ??

Grid search will be best I guess

Yes

can i implement these concept if i have continuous value as output ie if I want to do regression problem

Krish you are amazing, keep it up! One comment, I wouldn't take too high values ​​for the 'max_depth' parameter because of overfitting issues...

@akarshankumar1711

3 жыл бұрын

It's okay to take high values anyways it's random forest, a high variance base model is needed. And also it's precisely not depth but more related to num of leafs. Hence high value do more good than harm.

yes

hey question how do you get the predictive text?

But using tpot can I print the values of the hyper parameter for which our model has best accuracy...

Shouldn't we split our data before imputing any sort of values to prevent data leakage?

thank you sir...why dont i get accuracy value..? so there is no return value on loss

Best session please conduct such kind of class

how u r not getting error while u having 1 as int value in min_samples_split which is not allowed ! although i m getting this error (min_samples_split must be an integer greater than 1 or a float in (0.0, 1.0]; got the integer 1) which is right : we can either use 1.0 float or greater value than int 1

Telegram link is not opening

hi good evening

Good evening sir, I needed some guidance how can I can contact with you?

Starts at 11:30

can we do stratifiedkfold validation in gridsearchcv or randomsearchcv

@sahilp4796

3 жыл бұрын

Yes, we can use. Sending you a sample code for RandomizedSearchCV skf = StratifiedKFold(n_splits = 5, shuffle = True, random_state = 7) random_search = RandomizedSearchCV(model, param_distributions=params, n_iter=3, scoring='accuracy', n_jobs = -1, cv = skf.split(X_train, y_train), random_state=7)

I got an error :cant pickle file and send it to workers when i ran the randomsearch cv

@its_me7363

3 жыл бұрын

remove 'n_jobs' parameter.

I have tried hyper parameter and my laptop take 6 hrs to give the parameter what should I do ! It is wasting my time.

@NeuralNet_Ninjas

4 жыл бұрын

You can run the same code in kaggle.Kaggle provides free access to NVidia K80 GPUs in kernels

@MV-zm5jd

3 жыл бұрын

Try google colab

How to reduce the false positive and false negative

@NeuralNet_Ninjas

4 жыл бұрын

As per my understanding and knowledge, if your data is balanced in terms of proportion of two classes and if you have built very good model then automatically your false positive and false negative will be less. But if your data is imbalance, depending on the use case that you are working on you can increase or decrease the threshold to reduce false positive and false negative. But that's a tedious process , so better way is to look at ROC curve.

HIi Krish I got this error how to solved it. ValueError: Invalid parameter min_sample_split for estimator RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=None, max_samples=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=250, n_jobs=None, oob_score=False, random_state=None, verbose=0, warm_start=False). Check the list of available parameters with `estimator.get_params().keys()`.

@ai_beyond_boundaries

4 жыл бұрын

i also got the same error

@bharadwajnarayanam9922

3 жыл бұрын

Hi Hitesh! Can you show the code too?

@hiteshyerekar9810

3 жыл бұрын

@@bharadwajnarayanam9922 hiii I solved those problem.

@bharadwajnarayanam9922

3 жыл бұрын

@@hiteshyerekar9810 Cool bro!

7:33

finished coding

Yed

When you are pissed off of copy pasting things - 46:52

the lecture is good but shaking legs does not seem good kris.

Hello everyone, I get an error regarding accessing subscript for the randomizedsearchcv object, 1 from sklearn.model_selection import GridSearchCV 2 param_grid = { ----> 3 'criterion': [rf_randomcv['criterion']], 4 'max_depth': [rf_randomcv['max_depth']], 5 'max_features': [rf_randomcv['max_features']], TypeError: 'RandomizedSearchCV' object is not subscriptable

Yes