Random Forest Algorithm - Random Forest Explained | Random Forest in Machine Learning | Simplilearn

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[@ashishjain871]

Wow, the amount of effort to create these slides for teaching the material is obviously very high. Simply amazing :).

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This channel has one of the best machine learning videos available on the internet

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[@IthaliiJackson]

Sure, I can attest to this.

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[@monome3038]

never had any tutorial/lecture explaining so well, so simply yet so detailed; thank you so so so much !

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[@SimplilearnOfficial]

We hope this video was useful. The link for the dataset used in the video is provided in the description. Thanks!

[@anutseksharma2811]

Hi, Thanks for great explanation. I have a small doubt. when you split test train in Ln [8] and in ln [9] we get how much data we have in training and testing- i get it. but when I do it in my same example- each time number of training and testing data gets different. why is it so? sometimes training data comes 120 and testing 30, sometimes 118, 32 or sometimes something else. why is it so?

[@KillaniSurya]

Can you send me the Jupyter notebook file of code??

[@Hiyori___]

Amazing tutorial and best explanation ever with the fruits. Also I love how clearly you explain the code

[@SimplilearnOfficial]

Glad it was helpful!

[@hemilpatel925]

you are excellent in explaining the full process and code step to step. GREAT JOB.

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[@0GRANATE0]

31:07 instead of pd.factorize(train['species'])[0]; we could also use "hot encoding" right?

[@qone89]

This video is really well done in that the teaching quality is good and the instructor understands the level of beginners by explaining everything clearly and simply

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Hi Kyuhwan, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)

[@paragjp]

Hi, initially random forest concept will using fruits concept. But in IRIS flower example it should show how random forest is working with example and diagram first. It would help to understand easily.

[@KrishnoSarkar]

Very clear description of Random Forest technique and the codes

[@santosksingh]

You guys explain the concepts really well!!!

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We are glad you found our video helpful, Santhosh. Like and share our video with your peers and also do not forget to subscribe to our channel for not missing video updates. You can also explore our playlist for more Machine learning videos - kzbin.info/www/bejne/bXvLm3yEhdyZj7M.

[@ankitabhatia4525]

At 16:38 , on what basis is the prediction from Tree 2 cherries. If I see the inputs, the first split Color is not Red, so the condition yields false and thus the prediction is still orange.

[@Medhusalem]

I think it is a bit strange as well. First tree: Color(Orange) True, means red = false Second Tree: Color(Red) True, means orange = false That doesn't seem right to me, that it just guesses the color both times instead of sticking with one and using it through all the decision trees.

[@twbouji7580]

@@Medhusalem if we assume that it "chooses" randomly a color for each tree, then it makes sense. He said that they are good working with missing data, so is it possible that adding this randomness in the missing value a way to get the right prediction?

[@ganeshkumarpatel]

Dear simplilearn team here you put the best video to explain what Algorithms really are... But in LMS SELF PACED VIDEOS not so detailed explanation... Look into that and improve yourself

[@SimplilearnOfficial]

Thank you for letting us know know about this. Your feedback helps us get better. We are looking into this issue and hope to resolve it promptly and accurately.

[@0GRANATE0]

16:28 Why does it mark the (black fruit) as orange? I mean the data is missing? Does it pick this one Decision randomly? => If it would pick red, the whole example would not work, right?

[@philhearing3659]

You are a great lecturer, thank you for explanation!

[@SimplilearnOfficial]

Hey Filip, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)

[@nouhaylachataoui2821]

amazing explanation , so simply and detailed , thank you so much sir

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[@D4nte-RN]

Great skill with explaining everything in simple words!

[@bluevalley82]

Thank you so much m. I’ve learnt alot from you

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You are so welcome

[@yasirali8409]

Amazing way of explanation...

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Glad you liked it

[@Stephen-sd2xe]

Awesome tutorial by simplilearn. Thank you so much!

[@Loicmartins]

5 years after it's always very clear!

[@swatijha7390]

Hey, just awesome video ! Concept were explained clearly

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Glad you liked it!

[@TheRinkung]

So great explanation. Thank you!

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Hope you enjoyed our video! We have a ton more videos like this on our channel. We hope you will join our community!

[@abrahamofek4485]

Very impressive, thank you

[@SimplilearnOfficial]

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[@tracyc4458]

How does tree 1 decide the colour of the fruit is orange if the colour of the fruit is unknown? Do random forests consider all possible outcomes and take the majority of those? Thanks x

[@gezahagnnegash9740]

Thanks, it helps me a lot!

[@SimplilearnOfficial]

Glad it helped!

[@benjaminianashley5680]

I have a doubt with the Random Forest being able to cope with missing values. In many other places I have heard that you must replace any null values for models to work. I tested an example on another dataset with null values and got this error, "ValueError: Input contains NaN, infinity or a value too large for dtype('float32'). " . Please could you expand on this. Excellent Video - thanks :-)

[@harshassp9144]

if your data set is large then simply drop NAN rows

[@SimplilearnOfficial]

Thanks for your input!

[@SimplilearnOfficial]

Nan values cannot be compared with float32 type values. This is why it's important to remove all Nan values.

[@briancheloti136]

A very great tutorial indeed. I understood the explanation so well. Could I pease have the dataset and code for this tutorial?

[@SimplilearnOfficial]

Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.

[@andrewfoers8861]

Beautfiully explained. Thanks!

[@SimplilearnOfficial]

Glad it was helpful!

[@balajee41]

Great explanation. I have a question (1) At 15:40, how do we get split decision "Grows in summer"? This category variable is not available in dataset na?

[@SimplilearnOfficial]

Hi Balajee, we assume this factor is present only for the sake of understanding. Thanks.

[@blackdeath39muffin45]

Can't we use train_test_split to train the model instead of all the steps in the prep?

[@Siyavarramchandkijai]

I am not python person but no doubt your explanation of concept is simply awesome

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[@apurva_m]

Amazing explanation 👌

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[@azingo2313]

Convention....True on Left 😊

[@MSuriyaPrakaashJL]

so if the data is missing . Is the result TRUE always?

[@azwraithlance5159]

well i think its depend on accuracy of the model

[@yodoggydogg8490]

what if my data is already numerical what is the step to implement instead of factorizing?

[@esraagamal8938]

Appreciated , really i enjoy learning with you , keep going :) :)

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Glad you enjoyed our video! We have a ton more videos like this on our channel. We hope you will join our community!

[@hedijabnouni4370]

Thank you for this video. I have a practical work to do regarding my studies. The goal is to code a program with python concerning the image classification using Random Forest technique. Can you explain to me how to modify your code to use it on the pixels of images ? (we will test it on the famous image of Lena), and this is for the two phases: learning and evaluation according to the evaluation criteria of Levine and Nazif (Inter-region) Thank you in advance.

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[@zhuotunzhu8660]

Nice explanation!

[@SimplilearnOfficial]

Glad it was helpful!

[@kasyapdharanikota8570]

thank you , very well explained . found this very helpful .

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Glad it was helpful!

[@MeetPatel-sk7pu]

Awesome work done by u🔥

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Thank you so much 😀

[@joxa6119]

I have done Decision Tree before. Can I just change the classifier to Random Forest? Or I need to follow this one?

[@SimplilearnOfficial]

"Hi , You can leverage your decision tree, update the parameters and change it into a Random Forest Classifier."

[@shagun18jan]

Hey! can you explain, me why didn't we split tree on the basis of color at the root node instead of using diameter and then color in the example of where in the basket there were three fruits Apple, lemon and grapes. three of them had a different color so we could have split them on the basis of color and we have got accurate results. And there wouldn't have been any need to use diameter. Can you please clear this doubt of mine. Also, Can Iris flower data set be modeled using Support Vector Machine? If yes which model is better the random forest or Support Vector Machine

[@jjoshua95]

Nice explanation thanks!!

[@SimplilearnOfficial]

Glad it was helpful!

[@jessehahka]

Is it possible to predict a set of numbers that will output from a random number generator, finding the algorithm, in order to duplicate the same pattern of results?

[@IthaliiJackson]

Many Thanks. Nicely explained.

[@SimplilearnOfficial]

Hey Jackson, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)

[@adaloreen]

You have explained it very well but I have a question, why does the decision in 16:38 became cherries and yet the given parameters for its training set is given that the color of the unknown fruit is orange? thank you! I also need the answer because I will present this topic in our analytics class. thank you and more power! :D

[@xiaoyuwang8157]

I guess whenever the decision split is about color, it will automatically goes to true branch, since there is no color information in the inital input

[@pratikdani1746]

So, initially when the example begins narrator tells us that we do not know the color of the object, which is the missing data itself, so the decsion tree cannot figure out what color it is having and istead goes to the second branch of both but the branch on right has no further branches but the branch on the left goes to the next decesion and gives us the result cherries. I, hope this helps.

[@SimplilearnOfficial]

Although the colour for the unknown fruit is specified in the block containing data, for this example we assume that the colour is unknown. This is also mentioned in the audio. Therefore, our second decision tree makes the first split based on colour and arbitrarily says the fruit is red.

[@harsimranjeetsingh2693]

thank you for the tutorial, i have been subscribed to your channel for around a year now and i love the content, can you please send me the dataset for all the videos in this playlist that use Python.Thank you

[@SimplilearnOfficial]

Hello Harsimranjeet, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.

[@harsimranjeetsingh2693]

@@SimplilearnOfficial its [e-mail]

[@hashikamaduranga6122]

thanks a lot

[@SimplilearnOfficial]

You are most welcome

[@RafaelButao-o5b]

well explained, sir

[@SimplilearnOfficial]

We're so glad that you enjoyed your time learning with us! If you're interested in continuing your education and developing new skills, take a look at our course offerings in the description box. We're confident that you'll find something that piques your interest!

[@anthonysoronnadi5493]

Great teacher

[@SimplilearnOfficial]

Thank you! 😃

[@jianhongzhou9520]

I have a question about converting the species name into digits (0,1,2): what if we don't do the conversion? Can the classifier still do the prediction based on the species names(string)?

[@amortalbeing]

No, all of these models, operate on numbers. you must convert them into their numerical representation

[@SimplilearnOfficial]

Thanks for your input!

[@amortalbeing]

@@SimplilearnOfficial No, Thank 'YOU' for being such a great Channel. I Enjoyed extremely well. Keep up the great work

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[@temporarilyspatial]

For the random forest, shouldn't the same fruit bowls/datasets have the same classification trees? That is, shouldn't the same fruit bowl split the same way to maximize information gain/GINI index? In random forests, doesn't the machine aggregate decision trees built from different datasets?

[@SimplilearnOfficial]

Random forest creates multiple decision trees from a particular data set. Of course, each tree is formed considering a different section of the data set. Since different sections of the dataset are used to construct each classification tree, the fruit bowl will be split in different ways. random forest algorithm takes all the trees into consideration to generate the most accurate result.

[@keerthitippana7693]

25:50 I have a doubt on splitting data into Test and train. Here we are not splitting exactly into 75% and 25% of data. Here we split on random percentage of data. Why don't we use "train_test_split" from "sklearn.model_selection", where we can split the data into desired amount of test and train ? Thanks alot for the video.

[@0GRANATE0]

You got still no answer?

[@rishikambhampati2862]

A great tutorial to get an understanding of what random forest is. Great work and Thanks :)

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Hey Rishi, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)

[@gerardovera9829]

Hi, I run the same code for practicing but the prediction results are different, does anybody have any idea of why is this? Maybe due to changes in the packages versions? I get "setosa, setosa" instead of "versicolor, versicolor" in block "Out[36]"

[@aishasiddiquadabeer5143]

Thank you Simplilearn team for the clear explanation. Can you please provide the dataset and the python notebook used in the video?

[@SimplilearnOfficial]

Hello Aisha, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.

[@vashistnarayansingh5995]

Why can't you use the in inbuilt method of sklearn to split the data 8n training and test datasets

[@SimplilearnOfficial]

Hi Vashist, thanks for checking out our tutorial. You are indeed right. There are multiple ways to split the data and using sklearn's inbuilt function is surely one of them. Hope that helps!

[@jerrylin5089]

how did the 3rd tree figure out the color was orange? If it didn't know that, how was it able to classify the object as an orange??

[@sammy0722]

Nice explanation. But for deciding optimum level of trees in a Random Forest we use OOB error rate. Can you also include it in may be next video. Thanks.

[@neginalam4950]

Hi thank you. a wonderful tutorial. I have 9 features (unknown) and target. I want to predict if the customers will sign up or not. Do you think random forest can be applied here?

[@swatijha7390]

Try different model thn check which one give your desired output

[@divyadas986]

Can you show the overfitting and underfitting with python code

[@pravinjob5565]

why train_test_split is not used in this method? is there any specific reason

[@SabbirAhmedSibli]

In 17:07 First decision tree showing its color is orange that's true. then in the second tree why it is showing color=red is also true?

[@ramneeksingh3988]

Hi Thanks for this wonderful lecture but I have a query, won't a decision tree will always try to make a root node and following nodes in a manner where entropy is least? And I believe yes, then does it select root nodes at random and then follows an IG algorithm like ID3? How much 'Randomness' is there when Decision Tree decides which node will be root node, considering we have hundreds of nodes.

[@sachindoddamani2304]

Thank you! It was amazing with lots of information. Can I get access to the python code, please?

[@SimplilearnOfficial]

Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.

[@sachindoddamani2304]

@@SimplilearnOfficial [e-mail]

[@asmitamore9021]

Nice explanation 👌

[@SimplilearnOfficial]

Thank you 🙂

[@amilcarc.dasilva5665]

Great tutorial .....Great Tutor and well explained...I have subscribed this tutorial and I assure you that I have been learning so many things about algorithms in ML in the previous videos.......I really love this tutorial. I really appreciate also your kind help whenever I request for the datasets .......I wanna one clarification on the "load_iris" is this the in-built function (or library)...?

[@SimplilearnOfficial]

Hi Amilcar, thanks for subscribing to our channel and joining our community. We have shared the required dataset to your mail ID. Stay tuned for the updates!

[@amilcarc.dasilva5665]

@@SimplilearnOfficial many thanks. Got it.

[@SimplilearnOfficial]

Very welcome!

[@SimplilearnOfficial]

The iris dataset is present within the sklearn library as it's one of the most commonly used one. So yes, load_iris is an inbuilt method that loads the iris dataset.

[@venkatteja5885]

@@SimplilearnOfficial hello..great video..please send the python code and the file...

[@aakashnishad7048]

Thks sir

[@SimplilearnOfficial]

Very welcome!

[@nikhilkhemchandani5991]

could we use split function for train and testing set

[@arifshaik9986]

very good explanation sir. will u share the code and dataset please

[@SimplilearnOfficial]

Hello Arif, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.

[@MrPresonic]

Great Video, thank you! Off topic question: As a non-native Englisch speaker I am wondering if the way you pronounce mEAsuring is a certain dialect or the actual correct pronounciation.

[@Desi-qw9fc]

Peter Presonic it’s just his accent. Normal pronunciation is “meh”, not “may”.

[@SimplilearnOfficial]

Thanks Peter, we are glad you found this content useful. That is his accent :) We have come up with new videos on Machine Learning, do check it out here: kzbin.info/aero/PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy Happy learning from Simplilearn team!

[@tanujkalra7334]

Hello Sir!!! Can you please tell me,how did we figure out the unknown fruit as cherry at 16:37

[@Remmy1314]

First of all, the tree will ignore the missing data, since color unknown, it COULD BE true for the fruit to be apple or cherry. And then, with Circle, it COULD Be cherry. Trees tell what COULD Be true in according with the existing information.

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[@riasiti8369]

Terimakasih. Thank you!

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You are very welcome!

[@murtazawi.ch1]

The best explanation. Thanks for sharing.

[@SimplilearnOfficial]

Hey, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)

[@kaushikdwivedi1845]

why you did not use train_test_split here, and for changing species to 123 we can use is LabelEcoder I am right ps let me know

[@sujithkumar804]

Thankyou for the video . Can you explain why is that it has high accuracy .. is it because of bagging approach only or are there any other reasons behind it.

[@SimplilearnOfficial]

It is predominantly the bagging approach. The fact that the random forest algorithm works on different parts of the dataset also plays a role in providing better accuracy.

[@anuragpbox]

Can we have access to the notebook please?

[@SimplilearnOfficial]

Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.

[@anjithnair3082]

I have seen everyone use clf as the variable name for instantiating the random forest classifier. What is the abbreviation of CLF?? Just out of curiosity.

[@SimplilearnOfficial]

Hi Anjith, thanks for watching our video. CLF just stands for "classifier". Hope that clarifies your curiosity. Do support us by subscribing to our channel using this link: kzbin.info.

[@scigama71]

Excellent

[@SimplilearnOfficial]

Hey James, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)

[@AHElz-je1jh]

Hey. Thank you too much for this video. Can you write the codes to draw the random forest and branches of the decision tree also how save it as png or pdf file by python, please?

[@rahulpandey3079]

From where can i get the data sets used in all the videos from simplilearn? Fast help would be highly appriciated?

[@SimplilearnOfficial]

Hello Rahul, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.

[@stephengrey1102]

Great explanation. Is the python code available for download anywhere? Are random forests a good choice for binary classifiers? Or are there other algorithms that do a better job?

[@SimplilearnOfficial]

Hello Stephen, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.

[@riyalikhite1393]

perfect sir

[@SimplilearnOfficial]

Thank you!

[@mrugendrashilvant8358]

Hi, can you please tell me why you've taken the training data set to be 75% of the total set? How can we find out the optimum value of the training set?

[@SimplilearnOfficial]

"Hi Mrugendra, There is no predefined rule or an optimum value as to how much you should assign for training and testing. Ideally, the dataset is divided into (70, 75 or 80%) for training the model and (30, 25 or 20%) to test and validate the model. "

[@mrugendrashilvant8358]

@@SimplilearnOfficial Thank you! Great Work !!! Keep it up

[@elchopaxi5196]

Great video and explanations are top, but I can't run the code at 27:43, what is the problem if i may ask?

[@SimplilearnOfficial]

Hi Lethabo, thanks for appreciating our work. We have forwarded your query to our team. Be assured, your queries will be addressed.

[@Remmy1314]

Try to Separate the code from ## train , test to ....... ## train = df[df['is_train']==True] test = df[df['is_train']==False] hope it helps

[@SimplilearnOfficial]

We appreciate your help! Keep engaging with our channel and stay tuned for more. Cheers!

[@rezamaleki9241]

please share Python code for plotting confusion matrix for this example

[@SimplilearnOfficial]

Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.

[@HuskyAssassin1995]

Hi, can i ask at 31:27 when you execute clf.fit(train[features],y) what happens if Number of labels=______ does not match number of samples=_____?

[@jonbloom1870]

is the jupyter notebook available online ?

[@SimplilearnOfficial]

Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.

[@corymaklin7864]

Great video thank you

[@SimplilearnOfficial]

Hey Cory, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)

[@nesatdereli]

The tutorial is a pandas tutorial after 17:55 and before 17:55 it teaches the basics only. Would be better if you could provide some more details like how a tree is built or updated.

[@SimplilearnOfficial]

Hi Nesat, thank you for watching our video and for the honest feedback. We will definitely look into this. Do subscribe, like and share to stay connected with us. Cheers :)

[@RafaAyadi]

You guys are the bomb! Thanks!

[@SimplilearnOfficial]

Hey Rafa, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)

[@syedasadullah7025]

Hey i am doing traffic prediction and feature of matrix has days and weather condition in it can i apply random forest algorithm over it and also want to know that do i have to convert all days into 0-7 kindly reply soon

[@SimplilearnOfficial]

"Hi Syed, We would suggest not to opt from random forest to solve this particular problem since that features are very less. So, to split the data at a particular node would be different."

[@pranavwalunj8635]

I have a doubt at 16.37, check your output from tree 2 .

[@SimplilearnOfficial]

Yes. Looks right. Thanks for your input.

[@kakk5822]

Great Video,thank you and please share the dataset

[@SimplilearnOfficial]

Hi, we have shared the dataset to your mail ID. Happy Learning!

[@KingYWong-kw3fj]

Can you please send me the dataset as well? Thank you.

[@SimplilearnOfficial]

Hello Wong, thanks for watching our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. Cheers!

[@champachampa7402]

all features are not given to all trees. but here you have given so. How Tree1, tree2, tree3 is deciding not understood properly

[@SimplilearnOfficial]

Hello, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.

[@supernitt]

Do you have the random forest video in the part of the regression? Thanks.

[@SimplilearnOfficial]

Hi Kritchayan, we don't have random forest video in the part of regression. However, we have Random forest video made separately in both Python and R language. If you are interested, check the below links: Random Forest in Python: kzbin.info/www/bejne/m36Xpn1sjayhia8 Random Forest in R: kzbin.info/www/bejne/fpa3hWppjN1-f8U

[@s.e.7268]

well explained!!

[@SimplilearnOfficial]

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[@HollyVanHart]

👍 Awesome, thanks for this! 😊 💗 🙌

[@SimplilearnOfficial]

Hey Holly, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)