![Easy ML](/img/default-banner.jpg)
- Видео 93
- Просмотров 455 292
Easy ML
Добавлен 8 окт 2018
Видео
Data Normalization using Z-Score technique
Просмотров 20 тыс.5 лет назад
Data Normalization using Z-Score technique
Introduction to Supervised Learning
Просмотров 2,2 тыс.5 лет назад
Introduction to Supervised Learning
Introduction to Correlation Coefficients
Просмотров 2,5 тыс.5 лет назад
Introduction to Correlation Coefficients
Evaluating accuracy of Regression Models
Просмотров 17 тыс.5 лет назад
Evaluating accuracy of Regression Models
Evaluating efficiency of Regression Models
Просмотров 1,6 тыс.5 лет назад
Evaluating efficiency of Regression Models
Regression Models - Step 2 : Splitting Data
Просмотров 1,5 тыс.5 лет назад
Regression Models - Step 2 : Splitting Data
Regression Models - Introduction to Correlation
Просмотров 1,5 тыс.5 лет назад
Regression Models - Introduction to Correlation
Regression Models - Step 1 : Variable Selection (Part 1)
Просмотров 2,3 тыс.5 лет назад
Regression Models - Step 1 : Variable Selection (Part 1)
Regression Models - Step 1 : Variable Selection (Part 2)
Просмотров 1,7 тыс.5 лет назад
Regression Models - Step 1 : Variable Selection (Part 2)
Spurious Correlations - Why we need Regression Models ?
Просмотров 4,1 тыс.5 лет назад
Spurious Correlations - Why we need Regression Models ?
Introduction to types of Correlation
Просмотров 1,8 тыс.5 лет назад
Introduction to types of Correlation
Introduction to Random Forest Models - Understanding Decision Trees (Part 1)
Просмотров 1,3 тыс.5 лет назад
Introduction to Random Forest Models - Understanding Decision Trees (Part 1)
Introduction to Unsupervised Learning
Просмотров 2 тыс.5 лет назад
Introduction to Unsupervised Learning
Introduction to K-means - Choosing number of clusters
Просмотров 16 тыс.5 лет назад
Introduction to K-means - Choosing number of clusters
Evaluating Principal Component Analysis (PCA) - Part 1
Просмотров 1,2 тыс.5 лет назад
Evaluating Principal Component Analysis (PCA) - Part 1
Evaluating Principal Component Analysis (PCA) - Part 2
Просмотров 1 тыс.5 лет назад
Evaluating Principal Component Analysis (PCA) - Part 2
Introduction to Principal Component Analysis (PCA)
Просмотров 1,8 тыс.5 лет назад
Introduction to Principal Component Analysis (PCA)
Nice explanation; I loved the way you used the two extreme cases for clustering.
Sir, I have analysed 850 soil samples for different forms of soil acidity and 7 other soil properties like pH, EC, OC etc. Sir, I would like to predict Exchangeable acidity (numeric) with 7 soil parameters. How should I calculate random forest?
what is nc in WSS function?
Ok I got what is nc!!
Thank you ur vedio brought me a hope that R is easy
why the f you scream this much...
Thank you so much❤
Made it so simple and illustrative... thanks a lot
Hi, what is this "predict(...)" function? Is it from 'randomForest" or it's R built-in function? Thanks
Hello, Can you help me please
good explanation
What if i'm trying to load my personal data in?
can you please make this same video but in python...
My autoplot(KM, mydata, frame= TRUE) doesn't work. I run it and it doesn't do anything. I have to point out that I didn't run the wssplot function though. Is it because of that? I fixed it. It's because of my lack of R syntax knowledge! I have no idea why but I am running R in vscode and vscode doesn't print the variables if they are not in a print function.
good job, short and directly to the point. thank you
Hi, thank you very much for sharing this video :) the only one tutorial that I was able to follow. One question, my predicted variable is not categorial, but it´s an area of deforestation. So, Can I use the code you shared in this video?
Excellent explanation! Thank you!
So glad to see 85k views!
how can incorporate upsamling or downsampling before running the random forest model? neeeeed help pls
This whole series was so helpful - thank you so much!
Super helpful, thanks!
The mean of my cor(data) is 0.45. Is it not eligible for pca? What should i use for variable selection then?
Excellent video!! Thank you so much
29k views! 👏
Worst educational video I've seen for a while. And I've watch thousands.
are u stupid?
This was very very clear. I enjoy the examples showing the extreme scenarios to make the optimum example hit home.
Hansel is a thicc boy
You have some great videos. Why have you stopped posting videos?
This was so helpful thank you
After 2 hrs of surfing internet about the subject, i found this video and it clarified the concept in 3.11 minutes. Really Thank you
Nice explaination ❤
Sir, You are a Good TEACHER, Short and sweet explanation, Superb
us data analysts learning more than necessary about plant terminology..
useful. thank you. I will try this.
Hello, thanks for great video! It is heelpful. What do you suggest for stability test in r? Which function I can use?
Bang kalau pake data citra data raster bisa nggak?
Thank You very-very much! It is certainly one of the best explanations. Very helpful! But I have got some questions. 1) I can not understand what do we do next with the PCAs? Shall I use it for multiple regression along with other variables or for clustering? 2) Can I reevaluate impact of variables using loading data? For instance, I use 5 variables to build PCAs. My PCA1 and 2 describe about 85% of variability, but each PCA does not connect to - lets say - the 3rd variable. May be I should delete the 3rd variable and run the analysis with only 4 others? Will this improve the outcome? 3) Why are some spaces blank in loadings (minute 6.28 on the video) - like Sepal.Width vs Comp.1? 4) And the final - body mass index is given as an example of PCA outcome. Does that mean that we can retrieve PCA1 and name it as a sort of new stable variable? Or BMI is just an example of data dimension reduction that does not correspond to PCA directly? Thats a lot of questions - but I really wonder...
Hey thank you for such a detailed comment. At times I wish RUclips allows voice notes because typing would be ineffective per se. BMI is indeed just an example. The main application of PCA is to capture the essence of a large list of variables in fewer newly generated variables. Assume you have bank data.. there is a list of 120 variables and you need to predict loan delinquency. Inputting 120 variables and eventually tuning the model would be cumbersome in such instances you can deploy PCA to reduce the list of variables from 120 to let us say 12 and rest assured if you have executed PCA well then these 12 variables would have correctly captured the essence of the original 120 variables. The model that you will be building with these new set of PCs will be lighter and faster. You can deploy PCA before unsupervised learning as well !!
@@easyml1234 thanks! my point is - may be I don't need all 120. May be, I should extract 40 for one PCA and other 50 for another one and discard the remaining 30 and I will get better sketch of my "Eiffel tower" - that was a great example)) at least, I might get better values of my PC1 and PC2 equations. do we do so? reduce data for several PCAs? And once I get results of several PCAs - how do I interpret them? As variables for regression or basis for clustering?
@@easyml1234can this method used for categorical data ?
Thank you man, you finally made it made sense!
Thanks a lot for your comment Like / Share and Subscribe 🙏
Hello EasyML Awesome tutorial video. I wanted to know how can we determine the individual components of a cluster? What each of these blue and orange dots represents on the autoplot? Please help. Thanks in advance.
Thanks very much . Well explained and very useful
sir I feel there is some problem with the explanation here of the confusion matrix. The axis needs to be reversed.
1:14 - Number of predicted females are 15 and number of Actual Females are also 15 so that is right. I guess the confusion may stem from the fact that there are 3 values or boxes with 15 if I have filled it with different values it would have been easier to follow. The logic is correct but because of the similar numbering there could be some confusion. Anyway thanks for this !
Very helpful play list. Thanks a lot
Thank Sir
EXCELLENT tutorial I must say.... you are born with extraordinary God-gifted abilities my dear.
I had to stop the video to appreciate you for such a wonderful tutorial.
Full video here :- ruclips.net/video/NfIM9pUH9DA/видео.html
how do you determine what each cluster represents?
mode functions show "numeric" as answer :( help
even if my ears hurt by your hardcore indian accent, your way to explain is brilliant, sir!
While computing mean correlation you shouldve get rid of the diag and upper trig (?)
you are a life saver! Thank you for this simple explanation