One thing about this guy is that he gives very deep insights which you'll get nowhere else. As much as possible he'll give best practises, I have observed this from way back in the pandas course. Thanks so much Kevin. Please do deep learning and in-depth feature engineering tricks in a future video.
Best school too learn. I am learning it by my self as I I don'have enough bills toh py the fee. I have learned complete pandas from you thanks alooot, fantastic work and bless you
Your channel is great!! The videos are great for beginner and people whose English is not their native language because your voice is sooo clearrr to understand.
Thank you so much Kevin! Your response was very succinct and clear! I actually showed your video to my colleagues during our machine learning Friday sessions at work and we all loved it. It was a timely topic for us since we’re all fairly new to building ML models.
Feature selection and labelling is key ,so what steps we can take ? 1) Focus on question : What does it or you want,there may be so many features what matters to you most then drop the useless features for that project. 2)Visualize the data and plot . 3)Backtestting model : If time is not factor try various features and see the output.
1:25 "Now, why do you want to perform 'Feature Selection' in the first place?" The reason you do 'Feature Selection' is because removing irrelevant features results: (1) in a better performing model, (2) in an easy to understand model, and (3) in a model that runs faster. "So those are the three reasons for which 'Feature Selection' is useful."
In the presentation from Vishal Patel you are refering there is a workflow presented. I have two questions refering to the workflow (33:00 in the video): 1. What is the difference between pairwise correlation and multicollinearity. As far as I know to handle multicollinearity different pairwise correlation techniques (like pearson correlation coefficent, chi 2 or VIF) can be used. 2. Why would you perform either PCA or pairwise correlation/multicollinearity? If performing a PCA on (high) correlated data the output (principle components) still suffer from the (high) correlation eventhough the principle components itselfe are of course not correlated to each other. (imagen you do a PCA on 3 variables and 2 of them are highly correlated) Of cource the workflow diagram in the presentation is meant to be flexible as the whol feature selection process is, but could you still provide some thoughts to my questions. Many thanks, David
Hi Kevin, love the channel. But, there is a big difference between dimension reduction and feature selection. PCA, LCA are dimension reducing, they form part of the preprocessing steps, when you use PCA, the output is not a subset of the original feature set, it's a lower dimension of your data. Feature selection results in a subset of your features, LASSO, Elastic Net, Information Gain, etc are feature reducing. We normally talk about wrapper, embedded and filter methods in feature selection.
I'm familiar with all these terms, and I respectfully disagree with your point that feature selection is not dimensionality reduction. Dimensionality refers to the number of columns. Reducing that by any means is a reduction of dimensionality. I realize that some people use "dimensionality reduction" to only mean certain methods, but that doesn't change the fact that feature selection reduces the dimensions of your dataset.
This is a great video. The way you explain is very easy to understand. Great job! I just have a few questions to ask, if that's okay... How do you do feature selection on categorical variables? Is it a good idea to one hot encode them and then for example use the SelectKBest algorithm? (I've read that it isn't because it's not a good idea to remove dummy variables unless you drop only the first one) So yeah, are there any special algorithms that you use for feature selection for categorical variables or a mix of categorical and numerical variables in the dataset? In practice, do you first do feature selection and then one hot encode the variables?
You can use the top 10 most frequent categories and set everything else as “others”. It’s one work around. Or you can try and rank each of the categories using another feature. Then basically apply ordinal encoding. That way you dont increase the dimensionality and also ensure that even if the model gives more weightage to a category with a larger number, your model is correct because the weightage is already based on another feature from the dataset.
@@datascienceds7965 Hi Kevin, can you make videos on Time Series analysis with ARMA model, Customer behavior analysis with k means clustering and how to improve your random forest classifier with AdaBoost and Xg boost. Pls make your next videos on these topics.
Hi Kevin, Great video on feature selection techniques, but i have more complex question for feature selection strategy. I have a pool of 2k features and it turns out that according to business knowledge sometimes, the LOW CORRELATED FEATURES are more important than the HIGHLY CORRELATED ones. We use normal Pearson Correlation strategy to select the features but that always gives us the high correlated features when top 10 features are opted for. We need to improve on this and i am exploring SelectKBest Methodology as it helps in checking the significance of the correlation too. What else do you suggest, we can do in order to resolve such kind of issue!? Thanks, Atul
Awesome lesson! This topic is quite important in text classification while the number of words and phrases extracted from text are somehow overwhelmed.
Hey, I don't quite get this part "Tree based feature selection is only useful if that is your model that you're using or you could theoretically use a tree based model to look at feature importance, and then not actually use a tree based model for your model that you're building." Why is it? I think that because of those features are important (using tree based) then we can build a great model using tree based algorithm. Or maybe I am missing something here?
The point is this: You can use a tree-based model to determine feature importance, and those features are important, regardless of which model you decide to use. Hope that helps!
Hi you are a great teacher, very clear! I´m starting with DS and I want to ask you if you have the video of the presentation to share and deepen the topic of dimensionality reduction, thanks in advance, Kika
If you have two features to predict grass growth, like a Date column and a correlating amount of rain column, is that easy for an ML algorithm to understand? Or should you combine them to one column with categories, like "no rain", "little rain" etc. for the past 3 months?
Thank you for this insightful video. I am curious about whether there is a way to find the inputs that are most influential to the output of an ML model such as ANN. Is there a way to determine this?
Well, you can start by visualizing the data. It all depends on what kind of data you are working for. I highly recommend you to go on kaggle, look for the titanic dataset, and pick the most popular project. You will see how visualizing the data clearly helps choosing the features.
Hi can we use feature selections for unsupervised learning Clustering problem, where there is no target variable. Please let me know I will be highly thankful to you
@@dataschool Basically i completed the project on this and the best thing u can use for feature selection in such scenarios is looking two parameters i.e variance of a each feature and number of zeroes in each column
These are different Algorithms to select best features, but how to select the algorithm and when to use each of them? For example: if I have a multi-class classification problem where all the features are numerical and the output is categorical, which feature selection algorithm can I use?
Hi Kevin! Thanks for a very clear explanation. This video is very useful as I'm very new in machine learning. I have one question related to the feature selection. I started learning ML by implementing the decision tree. Most of the online tutorials just put all the features into the decision tree and let the DT select the features by itself. However, what if you have tons of features (let's say 100,000 variables), is it better to perform some feature selection before building the DT model? or it doesn't matter since DT can use Gini to automatically select the potential attribute to the model.
i am a phd student from ALGERIA and i d like to thank u for your helpfull vedeos and the effort you put to do them , can i ask you please to show us an example of how to build train and test an adaboost classifier in scikit learn like u did with knn and please can you tell us can we use SVM as a weak learner for adaboost ?? and how to make that weak learner loop in the classifier and compute those params error alpha of the weak learner and weight update ?? thanks in advance sir
Do you have any tips on how to handle datasets where there is a strong class imbalance? (ie. 95% of class A, 5% of class B?) Thanks, these videos are extremely helpful!
To handle class imbalance, you can try downsampling the majority class, upsampling the minority class, or techniques like SMOTE. Also, make sure you have chosen an appropriate evaluation metric. This video might help if you are doing classification with scikit-learn: ua-cam.com/video/85dtiMz9tSo/v-deo.html Glad you like the videos! :)
Aren't there ML libraries that can optimize the features? Ie by running and dropping various features and using that process to optimize features included in final model?
Hi Kevin, i am looking for how to create a feature list related to human error during production release. do you have any data which can help in forecast humar error or something looking at some historical incidents and deployement data.
Hello, Thanks for the explanation. I have one question. My question is, Does using best features helps to reduce the training data sets. Say I do not have a large datasets, but I can make independent variable that is highly corelated with the dependent variable, will it help me reduce my traning data sets. Your response will be highly valuable.
Is this possible to apply a custom loss function in a regression model ? I need to maximize a following function: if [predicted] < [actual] then [predicted] else [-actual]. Would that be possible ? Thanks
Hi. Thanks for your nice video. I am from India. I need help. If I want to filter data frame based one column with specific value (like: football) where number of times ouwn column value is max. How do I write. Please help.
Very useful video as I have taken over a machine learning project. Question if one technique such as correlation with target shows a feature to have little correlation but using say RFE shows it has importance - which should I trust?
Great question! It's hard to say - neither of those processes are guaranteed to be a reliable way of estimating the usefulness of a particular feature. That being said, my initial reaction is to trust the RFE score more, but it may depend on the particular situation. Hope that helps!
thnak you for your great video but i have a question : i'm want to use machine learning with econometrics to build a random forest classifier which method shouid i use for feature selection
Dear Evan I would like to do feature selection but my feature are categorical and also countinous is it possible to do 1 technique for the countinous feature and other for categorical? Or I have to convert all the features to categorical because there are total 40 features. I want the best 10.
sir, I have a dataset generated from 9 sensors, i.e it has 9 features (columns). if I make a subset of the dataset containing the maximum, minimum and some percentiles of each sensor (features), will it be called feature extraction. the new data set still has 9 features (columns), having less data (rows). if not what can we call it? this has been done to reduce computational cost.
Sir I learn lot of information about data science to see your videos.but sir i have some doubt about i hope you provide me a best information to remove my doubt. Qno1: what is the different between multi-colinearilty and PCA. Qno2: Is multi-colinearity and PCA is Same. Qno3: Is mulit-colinearity is only used for Regression model. Qno4: What are reason we did not used multicolinearity in our classification model
thank you for your nice video and with good presentation and i have question, have data set but the data does not have Labeled and i want to made feature selection for classification? how can i select features for unlabeled data
Great video. I learned so much in just one short video that would need a huge number of articles. One question, can you use ensemble models like decision trees and random forest to look at the feature importance and then use it to train another machine learning model (Say logistic regression)? Aren't the feature_importance given by an ensemble technique specific to themselves?
That's an excellent question! I think you are correct that feature importances are mostly model-specific, but you may still be able to apply that info to other models with some utility. Hope that helps!
Hi. I'm thinking about writing a bachelors thesis about using ML techniques to authenticate users based on keystroke dynamics. So you'd have CSV files that would be like: key down at timestamp A key up at timestamp B etc for a number of test subjects. This data should then be feature selected and fed to various ML Algorthims. I'm trying to picture what the features for this data would even be. LOL. Any ideas?
Hey Kevin, frist of all thank you sooo much for your videos! They are amazing! I got a little question to pairwise correlation and multicolinearity. If used already pairwise correlation and deleted attribute, which are highly correlated, its also nesscary to do a Multicolinearity test? Or would it be enough to use one of them, and when yes, which you ?
I'm working with a 2000 dimension data, Is it ok to use pca to reduce them to 50 and then use forward feature selection to further reduce to 20 or is it ok go from 2000 to 20 using pca itself?? Is it ok to use 2000 to 20 pca reduction method?
I am trying to learn machine learning on my own so I can't quite understand the steps you take. So based on what you said about choosing features, if one wants to eliminate features using forward selection should they know beforehand which algorithm they are going to use and try to do forward selection on the specific algorithm? Or should one do forward selection using logistic/linear regression and then having found the significant variables choose an algorithm (e.g Decision trees, kNN,..)? Thanks in advance.
@@dataschool Thanks so much for answering to my question. Can I please ask something more? So if I go with the former plan how am I going to choose which algorithm I want? I ve seen people advising to test all algorithms and see which performs better. Are you advising to test all algorithms having a full model with all features then choose the algorithm and then eliminate features or something else? Sorry I am a beginner and I don't know if I am asking something straight forward that everyone has already figured out..
No, everyone has definitely not figured this out :) You are asking a great question, but this is not a solved problem. This might be helpful to you: www.dataschool.io/comparing-supervised-learning-algorithms/
Hey Kevin, Thanks for your videos. They are extremely helpful. I have some knowledge on Python and Tableau and would like to switch my career to machine learning. I have been watching many videos on machine learning but confused from where to start. Please guide me how should I learn it stepwise. Thanks
I'll ask the author of the presentation for permission, and let you know... stay tuned! In the meantime, you can watch his full presentation here: ua-cam.com/video/ioXKxulmwVQ/v-deo.html
Even google can't provide so exact answer to the feature selection as you have comprehended in 10mins!!!!
Thank you so much!!!
You're very welcome! 👍
One thing about this guy is that he gives very deep insights which you'll get nowhere else. As much as possible he'll give best practises, I have observed this from way back in the pandas course. Thanks so much Kevin. Please do deep learning and in-depth feature engineering tricks in a future video.
Thank you so much for your kind words! 🙏 And thanks also for your suggestions, I'll keep them in mind!
Easily one of the best data science videos on UA-cam.
Thank you so much!
Unfortunately Most underrated channel on UA-cam.
You are too kind! 🙌
Very concise, right to the point, and no convoluted lingo. Thank you!
Thank you!
Best school too learn. I am learning it by my self as I I don'have enough bills toh py the fee. I have learned complete pandas from you thanks alooot, fantastic work and bless you
That's awesome to hear! Good for you!
Your channel is great!! The videos are great for beginner and people whose English is not their native language because your voice is sooo clearrr to understand.
Wow, thank you!
Hey, Kevin, your content is great. I did a whole project by taking help solely from your content 😊
That is awesome to hear! Congratulations on your project 🙌
Thank you so much Kevin! Your response was very succinct and clear! I actually showed your video to my colleagues during our machine learning Friday sessions at work and we all loved it. It was a timely topic for us since we’re all fairly new to building ML models.
You are very welcome, Davis! Thanks so much for sharing the video with others, and I'm so glad it was helpful!
Feature selection and labelling is key ,so what steps we can take ?
1) Focus on question : What does it or you want,there may be so many features what matters to you most then drop the useless features for that project.
2)Visualize the data and plot .
3)Backtestting model : If time is not factor try various features and see the output.
Great tutorial and the way you simplified entire dimensionality reduction aka feature selection is awesome
Glad it was helpful!
1:25
"Now, why do you want to perform 'Feature Selection' in the first place?"
The reason you do 'Feature Selection' is because removing irrelevant features results:
(1) in a better performing model,
(2) in an easy to understand model, and
(3) in a model that runs faster.
"So those are the three reasons for which 'Feature Selection' is useful."
I'm glad you like it, thanks.
Thanks for pulling out this quote!
This video was by far the best video on feature selection
Awesome, thanks so much! :)
great explanation. no extra unnecessary stuff
Glad it was helpful!
It is filled with information and is so easy to venture through. Thank you for making it available to all of us.
You're very welcome!
In the presentation from Vishal Patel you are refering there is a workflow presented. I have two questions refering to the workflow (33:00 in the video):
1. What is the difference between pairwise correlation and multicollinearity. As far as I know to handle multicollinearity different pairwise correlation techniques (like pearson correlation coefficent, chi 2 or VIF) can be used.
2. Why would you perform either PCA or pairwise correlation/multicollinearity? If performing a PCA on (high) correlated data the output (principle components) still suffer from the (high) correlation eventhough the principle components itselfe are of course not correlated to each other. (imagen you do a PCA on 3 variables and 2 of them are highly correlated)
Of cource the workflow diagram in the presentation is meant to be flexible as the whol feature selection process is, but could you still provide some thoughts to my questions.
Many thanks, David
These are excellent questions, but beyond what I have time to address in the UA-cam comments... sorry!
Just checked the database and I got 95,000 features after vectorization lol. Seems like I really need this one.
Yes!
Lot of insights in your lecture. Thanks
You're welcome!
Great video, again. Thanks so much for sharing these valuable tips.
You're very welcome! Glad it was helpful to you.
Hi Kevin, love the channel. But, there is a big difference between dimension reduction and feature selection. PCA, LCA are dimension reducing, they form part of the preprocessing steps, when you use PCA, the output is not a subset of the original feature set, it's a lower dimension of your data. Feature selection results in a subset of your features, LASSO, Elastic Net, Information Gain, etc are feature reducing. We normally talk about wrapper, embedded and filter methods in feature selection.
I'm familiar with all these terms, and I respectfully disagree with your point that feature selection is not dimensionality reduction. Dimensionality refers to the number of columns. Reducing that by any means is a reduction of dimensionality. I realize that some people use "dimensionality reduction" to only mean certain methods, but that doesn't change the fact that feature selection reduces the dimensions of your dataset.
Creative Content Not Reduntant at all Really Helpful
Thanks!
This is a great video. The way you explain is very easy to understand. Great job! I just have a few questions to ask, if that's okay...
How do you do feature selection on categorical variables? Is it a good idea to one hot encode them and then for example use the SelectKBest algorithm? (I've read that it isn't because it's not a good idea to remove dummy variables unless you drop only the first one)
So yeah, are there any special algorithms that you use for feature selection for categorical variables or a mix of categorical and numerical variables in the dataset?
In practice, do you first do feature selection and then one hot encode the variables?
You can use the top 10 most frequent categories and set everything else as “others”. It’s one work around. Or you can try and rank each of the categories using another feature. Then basically apply ordinal encoding. That way you dont increase the dimensionality and also ensure that even if the model gives more weightage to a category with a larger number, your model is correct because the weightage is already based on another feature from the dataset.
I did Recursive Feature Elimination with Cross Validation and Variance Inflation Factor for dimentionality reduction :-)
Those are two great suggestions - thanks for sharing! :)
@@dataschool you are welcome :-)
@@datascienceds7965 can we use RFE with grid search CV to select no. of features??
@@ElectronicsInside I don't know. I unfamiliar with it.
@@datascienceds7965 Hi Kevin, can you make videos on Time Series analysis with ARMA model, Customer behavior analysis with k means clustering and how to improve your random forest classifier with AdaBoost and Xg boost. Pls make your next videos on these topics.
Where to find the slides of "a practical guide to dimensionality reduction Vishal Patel " ? Thanks.
Hi Kevin,
Great video on feature selection techniques, but i have more complex question for feature selection strategy.
I have a pool of 2k features and it turns out that according to business knowledge sometimes, the LOW CORRELATED FEATURES are more important than the HIGHLY CORRELATED ones. We use normal Pearson Correlation strategy to select the features but that always gives us the high correlated features when top 10 features are opted for. We need to improve on this and i am exploring SelectKBest Methodology as it helps in checking the significance of the correlation too. What else do you suggest, we can do in order to resolve such kind of issue!?
Thanks,
Atul
Awesome lesson! This topic is quite important in text classification while the number of words and phrases extracted from text are somehow overwhelmed.
Thanks! You might like this video as well: ua-cam.com/video/ZiKMIuYidY0/v-deo.html
wonderfully explained!!
Thank you!
easy to understand your explanation thank you !
You're welcome!
Hey, I don't quite get this part
"Tree based feature selection is only useful if that is your model that you're using or you could theoretically use a tree based model to look at feature importance, and then not actually use a tree based model for your model that you're building."
Why is it? I think that because of those features are important (using tree based) then we can build a great model using tree based algorithm. Or maybe I am missing something here?
The point is this: You can use a tree-based model to determine feature importance, and those features are important, regardless of which model you decide to use. Hope that helps!
Really good tips for feature selection.
Thanks!
Hi you are a great teacher, very clear! I´m starting with DS and I want to ask you if you have the video of the presentation to share and deepen the topic of dimensionality reduction, thanks in advance, Kika
Thanks for your kind words! No, I don't have a video on that topic, sorry!
Awesome explaination of concept
Thanks!
this guy embodies the look of a data scientist
If you have two features to predict grass growth, like a Date column and a correlating amount of rain column, is that easy for an ML algorithm to understand? Or should you combine them to one column with categories, like "no rain", "little rain" etc. for the past 3 months?
Hey..thanks for the video. Can you make a video on how to identify multicollinearity, correlation etc from the dataset?
Thanks for your suggestion!
Thank you for this insightful video. I am curious about whether there is a way to find the inputs that are most influential to the output of an ML model such as ANN. Is there a way to determine this?
Well, you can start by visualizing the data. It all depends on what kind of data you are working for. I highly recommend you to go on kaggle, look for the titanic dataset, and pick the most popular project. You will see how visualizing the data clearly helps choosing the features.
Do features have to be stationary when applying ML models to time series data?
Hi can we use feature selections for unsupervised learning Clustering problem,
where there is no target variable.
Please let me know I will be highly thankful to you
I'm not sure, sorry!
@@dataschool
Basically i completed the project on this and the best thing u can use for feature selection in such scenarios is looking two parameters i.e variance of a each feature and number of zeroes in each column
These are different Algorithms to select best features, but how to select the algorithm and when to use each of them? For example: if I have a multi-class classification problem where all the features are numerical and the output is categorical, which feature selection algorithm can I use?
Depends on what library you are using. For scikit-learn, see here: scikit-learn.org/stable/modules/feature_selection.html
Hope that helps!
Hi Kevin,
Great respect 4 u. Y haven't u touched unsupervised and reinforcement topics? Expecting it.
Hi Kevin! Thanks for a very clear explanation. This video is very useful as I'm very new in machine learning.
I have one question related to the feature selection. I started learning ML by implementing the decision tree. Most of the online tutorials just put all the features into the decision tree and let the DT select the features by itself. However, what if you have tons of features (let's say 100,000 variables), is it better to perform some feature selection before building the DT model? or it doesn't matter since DT can use Gini to automatically select the potential attribute to the model.
That's a great question! Doing feature selection first is likely to help.
@@dataschool Thanks Kevin! I will try to implement it and compare the performance!
Thank you for your great and helpful videos
You're very welcome!
i am a phd student from ALGERIA and i d like to thank u for your helpfull vedeos and the effort you put to do them , can i ask you please to show us an example of how to build train and test an adaboost classifier in scikit learn like u did with knn and please can you tell us can we use SVM as a weak learner for adaboost ?? and how to make that weak learner loop in the classifier and compute those params error alpha of the weak learner and weight update ?? thanks in advance sir
Thanks for your suggestion!
Do you have any tips on how to handle datasets where there is a strong class imbalance? (ie. 95% of class A, 5% of class B?) Thanks, these videos are extremely helpful!
To handle class imbalance, you can try downsampling the majority class, upsampling the minority class, or techniques like SMOTE. Also, make sure you have chosen an appropriate evaluation metric. This video might help if you are doing classification with scikit-learn: ua-cam.com/video/85dtiMz9tSo/v-deo.html
Glad you like the videos! :)
Like your accent very much, keep going!
Thank you!
how i can evaluate the correlation between a
quantitative variable and
qualitative variable ?
A big thank to you Brother
the way of Superior teaching!
Thanks!
Aren't there ML libraries that can optimize the features? Ie by running and dropping various features and using that process to optimize features included in final model?
Hi Kevin, i am looking for how to create a feature list related to human error during production release. do you have any data which can help in forecast humar error or something looking at some historical incidents and deployement data.
Hi ,
I have data set which having most of the data is in negative and exponential columns as features for multiclass classification
How to deal with them
Sorry, I won't be able to help... good luck!
Hello, Thanks for the explanation. I have one question. My question is, Does using best features helps to reduce the training data sets. Say I do not have a large datasets, but I can make independent variable that is highly corelated with the dependent variable, will it help me reduce my traning data sets. Your response will be highly valuable.
Great talk!
Any thoughts on tsfresh library?
I'm not familiar with tsfresh, sorry!
@@dataschool its a cool way to automatically select and validate features - you might find it really useful
Great one Kevin. When are you going to do one on time series?
Thanks for the suggestion! You might find these videos to be useful: ua-cam.com/play/PL5-da3qGB5IBITZj_dYSFqnd_15JgqwA6.html
Is this possible to apply a custom loss function in a regression model ? I need to maximize a following function: if [predicted] < [actual] then [predicted] else [-actual]. Would that be possible ? Thanks
Hi. Thanks for your nice video. I am from India. I need help.
If I want to filter data frame based one column with specific value (like: football) where number of times ouwn column value is max. How do I write. Please help.
I'm sorry, I don't quite understand your question... good luck!
Great video!
Very useful video as I have taken over a machine learning project.
Question if one technique such as correlation with target shows a feature to have little correlation but using say RFE shows it has importance - which should I trust?
Great question! It's hard to say - neither of those processes are guaranteed to be a reliable way of estimating the usefulness of a particular feature. That being said, my initial reaction is to trust the RFE score more, but it may depend on the particular situation. Hope that helps!
thnak you for your great video but i have a question : i'm want to use machine learning with econometrics to build a random forest classifier
which method shouid i use for feature selection
Hard for me to say, sorry!
could you provide the book you explained it
Dear Evan I would like to do feature selection but my feature are categorical and also countinous is it possible to do 1 technique for the countinous feature and other for categorical? Or I have to convert all the features to categorical because there are total 40 features. I want the best 10.
sir, I have a dataset generated from 9 sensors, i.e it has 9 features (columns). if I make a subset of the dataset containing the maximum, minimum and some percentiles of each sensor (features), will it be called feature extraction. the new data set still has 9 features (columns), having less data (rows). if not what can we call it? this has been done to reduce computational cost.
That's feature engineering!
Great video
Thanks!
Nice Sir! Love and Respect from India ❣️
Thanks!
So does that mean we *may* do this on every dataset, or is it imperative that we do all of this in all datasets?
You should do it when it's useful, but no, you don't need to do it on every dataset.
Sir I learn lot of information about data science to see your videos.but sir i have some doubt about i hope you provide me a best information to remove my doubt.
Qno1: what is the different between multi-colinearilty and PCA.
Qno2: Is multi-colinearity and PCA is Same.
Qno3: Is mulit-colinearity is only used for Regression model.
Qno4: What are reason we did not used multicolinearity in our classification model
Sorry, I can't summarize any of these topics in a UA-cam comment. But they are great questions!
thank you for your nice video and with good presentation and i have question, have data set but the data does not have Labeled and i want to made feature selection for classification? how can i select features for unlabeled data
I'd do cluster analysis. Or search in google methods for unsupervised learning :)
I always appreciate you....
Thank you!
Great video. I learned so much in just one short video that would need a huge number of articles. One question, can you use ensemble models like decision trees and random forest to look at the feature importance and then use it to train another machine learning model (Say logistic regression)? Aren't the feature_importance given by an ensemble technique specific to themselves?
That's an excellent question! I think you are correct that feature importances are mostly model-specific, but you may still be able to apply that info to other models with some utility. Hope that helps!
Please demonstrate Features selection techniques in Python or in Matlab
Hi, is the presentation available?
Hi. I'm thinking about writing a bachelors thesis about using ML techniques to authenticate users based on keystroke dynamics.
So you'd have CSV files that would be like: key down at timestamp A key up at timestamp B etc for a number of test subjects.
This data should then be feature selected and fed to various ML Algorthims.
I'm trying to picture what the features for this data would even be. LOL. Any ideas?
Hey Kevin, frist of all thank you sooo much for your videos! They are amazing! I got a little question to pairwise correlation and multicolinearity. If used already pairwise correlation and deleted attribute, which are highly correlated, its also nesscary to do a Multicolinearity test? Or would it be enough to use one of them, and when yes, which you ?
I have the exact same question. To me 3 (pairwise correlation) & 4 (multicolinearity) are the same thing. I don't see any difference
Thank you
You're welcome!
hi
can you please let me know how to start the project in data science for bike sharing in detail with step by step
Sorry, I won't be able to help, good luck!
I'm working with a 2000 dimension data, Is it ok to use pca to reduce them to 50 and then use forward feature selection to further reduce to 20 or is it ok go from 2000 to 20 using pca itself??
Is it ok to use 2000 to 20 pca reduction method?
There's no universal answer to how it "should" be done, but I think just using PCA would be preferable.
I am trying to learn machine learning on my own so I can't quite understand the steps you take. So based on what you said about choosing features, if one wants to eliminate features using forward selection should they know beforehand which algorithm they are going to use and try to do forward selection on the specific algorithm? Or should one do forward selection using logistic/linear regression and then having found the significant variables choose an algorithm (e.g Decision trees, kNN,..)? Thanks in advance.
Great question! The former is usually a better plan.
@@dataschool Thanks so much for answering to my question. Can I please ask something more? So if I go with the former plan how am I going to choose which algorithm I want? I ve seen people advising to test all algorithms and see which performs better. Are you advising to test all algorithms having a full model with all features then choose the algorithm and then eliminate features or something else? Sorry I am a beginner and I don't know if I am asking something straight forward that everyone has already figured out..
No, everyone has definitely not figured this out :) You are asking a great question, but this is not a solved problem. This might be helpful to you: www.dataschool.io/comparing-supervised-learning-algorithms/
@@dataschool thank you, I love the table you made. I think I am getting into understanding this a bit more.
Great to hear!
How to work with Plotly and Cufflinks in visual studio code ??
I have no idea, sorry!
@@dataschool Can you please make videos on Decision Trees, Random Forests, SVM, Recommender Systems and PCA???
Thanks for your suggestion!
great video
Thanks!
Hello Kevin, Can you make a video on finding multicollinearity with VIF using sklearn library or may be with some other library.
Thanks for your suggestion!
Great post
Thanks!
Good one !!
Thanks!
how about LDA?
Thanks dear!
You're welcome!
Thank you ^^
You're welcome!
Why would you skip PCA?
Can you please explain in detail about Onehot encoding various features in detail because it would be helpful for many , Thank you
Thanks for your suggestion!
Sir,can you make video on data visualizatuin using all distributions of statistics? ?
Thanks for your suggestion!
Thanks for the help .......
You're welcome!
Can you provide me this presentation ppt for my personal use. Please sir
That speech clarity
Thanks!
Hey Kevin, Thanks for your videos. They are extremely helpful. I have some knowledge on Python and Tableau and would like to switch my career to machine learning. I have been watching many videos on machine learning but confused from where to start. Please guide me how should I learn it stepwise. Thanks
This might be helpful to you: www.dataschool.io/launch-your-data-science-career-with-python/
It would ne more awesome if you had done coding part too
another way would be the automated backward elimination with a loop
That's right - backward selection is another option. Thanks for sharing!
There's an Indian everywhere. Vishal Patel is an Indian 🤩
Saw you on Krish Naik's channel as well
@@tejas8211 yo thanks mate 😀😀
Great
Thanks!
could you please share this ppt with us
I'll ask the author of the presentation for permission, and let you know... stay tuned! In the meantime, you can watch his full presentation here: ua-cam.com/video/ioXKxulmwVQ/v-deo.html
Good news, I received permission to share the slides! Here they are: www.slideshare.net/VishalPatel321/feature-reduction-techniques
Kevin is a G
Best one :))
XGboost model automatically calculates feature importance
Great point! That makes sense, since it uses an ensemble of decision trees.