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

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This Random Forest Algorithm tutorial will explain how the Random Forest algorithm works. By the end of this video, you will be able to understand what is Machine Learning, what is a Classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples, and how to implement a Random Forest algorithm in Machine Learning. This video is a part of the Machine Learning with Python Series.
Below are the topics covered in this Random Forest Algorithm tutorial:
00:00 - 02:08 Applications of Random Forest Algorithm
02:08 - 02:59 Agenda
02:59 - 04:07 Classification Algorithms
04:07 - 05:36 Why Random Forest?
05:36 - 06:40 What is Random Forest Algorithm?
06:40 - 11:01 What is a Decision Tree?
11:01 - 14:18 How does the Decision Tree algorithm work?
14:18 - 17:27 How does the Random Forest algorithm work?
17:27 - 45:34 Use Case - IRIS Flower Analysis using Python
Dataset Link - drive.google.com/drive/folder...
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#RandomForestAlgorithm #MachineLearningAlgorithm #DataScience #SimplilearnMachineLearning #MachineLearningCourse #Simplilearn
What is Random Forest Algorithm?
The random forest algorithm is a supervised machine learning algorithm that takes randomly selected data and creates different decision trees. It then makes the collection of votes from trees to decide the class of the test object.
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Опубликовано:

10 июл 2024

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Комментарии : 524

@SimplilearnOfficial 2 года назад

🔥Explore Our FREE Courses With Completion Certificate: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE--caxhMlw_04.html

@ashishjain871 4 года назад

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

@SimplilearnOfficial 4 года назад

WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every week on all your favorite topics. Whenever you have the time, you must also check out our blog page @simplilearn.com and tell us what you think. Have a good day!

@monome3038 3 года назад

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

@SimplilearnOfficial 3 года назад

Thank you for the appreciation. You can check our videos related to various technologies and subscribe to our channel to stay updated with all the trending technologies.

@SimplilearnOfficial 3 года назад

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

@anutseksharma2811 2 года назад

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 2 года назад

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

@kaustavsarkar8732 4 года назад

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

@SimplilearnOfficial 4 года назад

WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every week on all your favorite topics. Whenever you have the time, you must also check out our blog page @www.simplilearn.com and tell us what you think. Have a good day!

@IthaliiJackson 4 года назад

Sure, I can attest to this.

@SimplilearnOfficial 4 года назад

Thanks for your love and support!

@Hiyori___ 3 года назад

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

@SimplilearnOfficial 3 года назад

Glad it was helpful!

@paragjp 5 лет назад

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.

@hemilpatel925 4 года назад

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

@SimplilearnOfficial 4 года назад

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

@santosksingh 6 лет назад

You guys explain the concepts really well!!!

@SimplilearnOfficial 6 лет назад

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 - ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-7JhjINPwfYQ.html.

@qone89 4 года назад

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

@SimplilearnOfficial 4 года назад

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 :)

@philhearing3659 5 лет назад

You are a great lecturer, thank you for explanation!

@SimplilearnOfficial 5 лет назад

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 :)

@KrishnoSarkar Год назад

Very clear description of Random Forest technique and the codes

@Stephen-sd2xe Год назад

Awesome tutorial by simplilearn. Thank you so much!

@d.p.1980 7 месяцев назад

Great skill with explaining everything in simple words!

@0GRANATE0 4 года назад

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

@swatijha7390 4 года назад

Hey, just awesome video ! Concept were explained clearly

@SimplilearnOfficial 4 года назад

Glad you liked it!

@murtazawi.ch1 6 лет назад

The best explanation. Thanks for sharing.

@SimplilearnOfficial 6 лет назад

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 :)

@esraagamal8938 4 года назад

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

@SimplilearnOfficial 4 года назад

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

@HollyVanHart 5 лет назад

👍 Awesome, thanks for this! 😊 💗 🙌

@SimplilearnOfficial 5 лет назад

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

@ankitabhatia4525 5 лет назад

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 4 года назад

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 4 года назад

@@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?

@TheRinkung 2 года назад

So great explanation. Thank you!

@SimplilearnOfficial 2 года назад

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

@bluevalley82 2 года назад

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

@SimplilearnOfficial 2 года назад

You are so welcome

@andrewfoers8861 3 года назад

Beautfiully explained. Thanks!

@SimplilearnOfficial 3 года назад

Glad it was helpful!

@kasyapdharanikota8570 2 года назад

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

@SimplilearnOfficial 2 года назад

Glad it was helpful!

@nouhaylachataoui2821 Год назад

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

@SimplilearnOfficial Год назад

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!

@ganeshkumarpatel 4 года назад

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 4 года назад

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.

@ramneeksingh3988 5 лет назад

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.

@RafaAyadi 5 лет назад

You guys are the bomb! Thanks!

@SimplilearnOfficial 5 лет назад

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 :)

@IthaliiJackson 4 года назад

Many Thanks. Nicely explained.

@SimplilearnOfficial 4 года назад

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

@gezahagnnegash9740 2 года назад

Thanks, it helps me a lot!

@SimplilearnOfficial 2 года назад

Glad it helped!

@HuskyAssassin1995 4 года назад

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=_____?

@abrahamofek4485 2 года назад

Very impressive, thank you

@SimplilearnOfficial 2 года назад

Glad you liked it!

@jjoshua95 4 года назад

Nice explanation thanks!!

@SimplilearnOfficial 4 года назад

Glad it was helpful!

@yasirali8409 2 года назад

Amazing way of explanation...

@SimplilearnOfficial 2 года назад

Glad you liked it

@MeetPatel-sk7pu 3 года назад

Awesome work done by u🔥

@SimplilearnOfficial 3 года назад

Thank you so much 😀

@riasiti8369 5 лет назад

Terimakasih. Thank you!

@SimplilearnOfficial 5 лет назад

You are very welcome!

@rishikambhampati2862 5 лет назад

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

@SimplilearnOfficial 5 лет назад

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 :)

@balajee41 5 лет назад

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 5 лет назад

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

@jessehahka 4 года назад

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?

@zhuotunzhu8660 Год назад

Nice explanation!

@SimplilearnOfficial Год назад

Glad it was helpful!

@shagun18jan 5 лет назад

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

@sujithkumar804 5 лет назад

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 5 лет назад

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.

@corymaklin7864 5 лет назад

Great video thank you

@SimplilearnOfficial 5 лет назад

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

@lalitheroes2011 5 лет назад

Welll.......Explained 👌👌👌

@SimplilearnOfficial 5 лет назад

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

@AntonioAndrade 6 лет назад

Nice video!

@SimplilearnOfficial 6 лет назад

Greetings! Thank you for your kind words. Spread the word by liking, sharing and subscribing to our channel! Cheers :)

@apurva_m 2 года назад

Amazing explanation 👌

@SimplilearnOfficial 2 года назад

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

@benjaminianashley5680 5 лет назад

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 5 лет назад

if your data set is large then simply drop NAN rows

@SimplilearnOfficial 5 лет назад

Thanks for your input!

@SimplilearnOfficial 5 лет назад

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

@tracyc4458 4 года назад

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

@0GRANATE0 4 года назад

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?

@anthonysoronnadi5493 3 года назад

Great teacher

@SimplilearnOfficial 3 года назад

Thank you! 😃

@user-vb5pn7js1l 9 месяцев назад

well explained, sir

@SimplilearnOfficial 9 месяцев назад

@blackdeath39muffin45 2 года назад

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

@gerardovera9829 3 года назад

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]"

@aakashnishad7048 5 лет назад

Thks sir

@SimplilearnOfficial 5 лет назад

Very welcome!

@Siyavarramchandkijai 4 года назад

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

@SimplilearnOfficial 4 года назад

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

@s.e.7268 4 года назад

well explained!!

@SimplilearnOfficial 4 года назад

Thanks a lot. Do subscribe to our channel and stay tuned.

@asmitamore9021 4 года назад

Nice explanation 👌

@SimplilearnOfficial 4 года назад

Thank you 🙂

@RS-el7iu 4 года назад

excellently explained.... would have been even nicer if split train/test was also shown in sklearn, also some evaluation criterias also from sklearn. thanks a lot...

@SimplilearnOfficial 4 года назад

Hey Raffi, 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 :)

@briancheloti136 3 года назад

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

@SimplilearnOfficial 3 года назад

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.

@temporarilyspatial 5 лет назад

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 5 лет назад

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 4 года назад

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 4 года назад

You got still no answer?

@Loicmartins 6 месяцев назад

5 years after it's always very clear!

@mandilal94 6 лет назад

awesome video

@SimplilearnOfficial 6 лет назад

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

@MrPresonic 6 лет назад

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 6 лет назад

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

@SimplilearnOfficial 6 лет назад

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: ru-vid.com/group/PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy Happy learning from Simplilearn team!

@hashikamaduranga6122 3 года назад

thanks a lot

@SimplilearnOfficial 3 года назад

You are most welcome

@aishasiddiquadabeer5143 4 года назад

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

@SimplilearnOfficial 4 года назад

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.

@scigama71 4 года назад

Excellent

@SimplilearnOfficial 4 года назад

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

@adaloreen 5 лет назад

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 5 лет назад

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 5 лет назад

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 5 лет назад

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.

@AHElz-je1jh 4 года назад

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?

@amilcarc.dasilva5665 5 лет назад

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 5 лет назад

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 5 лет назад

@@SimplilearnOfficial many thanks. Got it.

@SimplilearnOfficial 5 лет назад

Very welcome!

@SimplilearnOfficial 5 лет назад

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 5 лет назад

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

@hedijabnouni4370 3 года назад

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.

@SimplilearnOfficial 3 года назад

Glad you enjoyed

@riyalikhite1393 4 года назад

perfect sir

@SimplilearnOfficial 4 года назад

Thank you!

@sammy0722 4 года назад

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 4 года назад

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 4 года назад

Try different model thn check which one give your desired output

@nikhilkhemchandani5991 4 года назад

could we use split function for train and testing set

@supernitt 5 лет назад

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

@SimplilearnOfficial 5 лет назад

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: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-eM4uJ6XGnSM.html Random Forest in R: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-HeTT73WxKIc.html

@md.ibrahimullah9318 4 года назад

I really liked your slides :p :p

@SimplilearnOfficial 4 года назад

Hi Ibrahim, we appreciate the kind comment! enjoy!

@harsimranjeetsingh2693 4 года назад

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 4 года назад

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 4 года назад

@@SimplilearnOfficial its harsimranjeet1996@gmail.com

@vashistnarayansingh5995 5 лет назад

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

@SimplilearnOfficial 5 лет назад

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!

@azingo2313 Год назад

Convention....True on Left 😊

@FaycelMtar 3 года назад

Very good

@SimplilearnOfficial 3 года назад

Thank you for watching!

@anjithnair3082 5 лет назад

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 5 лет назад

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: ru-vid.com.

@rahulpandey3079 5 лет назад

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

@SimplilearnOfficial 5 лет назад

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.

@jianhongzhou9520 5 лет назад

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 5 лет назад

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

@SimplilearnOfficial 5 лет назад

Thanks for your input!

@amortalbeing 5 лет назад

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

@SimplilearnOfficial 5 лет назад

Hello, we are so happy to receive this wonderful compliment. Like and share our video with your peers and also do not forget to subscribe to our channel for not missing video updates. We will be coming up with more such videos. Cheers!

@soufyaneyassin7230 4 года назад

hello, thank you for this amazing video, can i get the powerpoint presentation? because i can not download it from slideshare

@SimplilearnOfficial 4 года назад

Hi Soufiane, we are not authorized to share the PPT materials. You can view it through slideshare. Thanks.

@kakk5822 5 лет назад

Great Video,thank you and please share the dataset

@SimplilearnOfficial 5 лет назад

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

@KingYWong-kw3fj 5 лет назад

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

@SimplilearnOfficial 5 лет назад

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!

@yodoggydogg8490 2 года назад

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

@sachindoddamani2304 3 года назад

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

@SimplilearnOfficial 3 года назад

@sachindoddamani2304 3 года назад

@@SimplilearnOfficial sachinrdoddamani@gmail.com

@joxa6119 2 года назад

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

@SimplilearnOfficial 2 года назад

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

@stephengrey1102 4 года назад

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 4 года назад

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.

@pravinjob5565 4 года назад

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

@Longfet53 5 лет назад

Why does tree #2 classify the fruit as cherries? the color of the fruit is orange

@SimplilearnOfficial 5 лет назад

Hi, thanks for checking out our tutorial. As mentioned in the video, for this particular example we must assume that the colour of the fruit is not known. So the fruit is randomly categorised as red. Hope that helps!

@dattabhabal421 5 лет назад

how to split the dataset with specific column using panda dataframe your code nt working

@omkareshpali8486 4 года назад

df= pd.read_csv('File_name') To access a specific column use:- df['column_name'] To access all values of that column use df['column_name'].values

@jerrylin5089 5 лет назад

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??

@tanujkalra7334 5 лет назад

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

@Remmy1314 4 года назад

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.

@SimplilearnOfficial 4 года назад

We appreciate your effort on sharing your knowledge. Do show your love by subscribing our channel using this link: ru-vid.com and don't forget to hit the like button as well. Cheers!

@elchopaxi5196 5 лет назад

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 5 лет назад

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

@Remmy1314 4 года назад

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 4 года назад

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

@MSuriyaPrakaashJL 4 года назад

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

@azwraithlance5159 4 года назад

well i think its depend on accuracy of the model

@arifshaik9986 4 года назад

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

@SimplilearnOfficial 4 года назад

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.

@syedasadullah7025 4 года назад

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 4 года назад

"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."

@nigiledwin4784 5 лет назад

Excelent lecture thanks.Can you please send me the code and data set for practice

@SimplilearnOfficial 5 лет назад

Hello Nigil, 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.