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I guess im randomly asking but does any of you know of a trick to log back into an instagram account..? I somehow forgot the account password. I appreciate any help you can offer me
Thank you for sharing this video it was very clear in detailed manner with example. But I have a doubt in whether I to should take squareroot of n (n = Sum of Output of Distance Function) for calculating K. 5:11 (Choosing K) 8:55 (Choosing K=3)
My professor took 1 hour to clear the basic concepts of KNN but I was unable to understand. Thanks for clearing my concepts in just under 15 minutes. Thanks a lot. Really appreciated. I am going to subscribe your channel. Thanks once again.
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Thanks alot that's very helpful, but when trying to use StandardScaler an error occurs "ValueError: could not convert string to float" i can't solve it , ahat shall i do ? thanks in advance.
i know im late but anyways it occurs because string data cant be standaradized i.e. put into StandardScaler. comvert it to float value try doing this: df[column_name] = df[column_name].astype('float') (i didnt try it myself but it should hopefull solve the issue)
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Thanks for a *fantastic* video!!! may I ask - when determining K, why do you do the sqrt of y_test, rather than y_train (or x_train, which is the same length). In the video, it looks like you intended to do that, then for some reason - changed it...
We are glad you found our video helpful. 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!
In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as an error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning one. Hope that helps!
20:32 Correction - standard scaler does not restrict the range of data between -1 to +1 . It converts the data to a mean of 0 and standard deviation of 1 . So if u take the mean of a standardised column ul find it equals 0. It basically skews the data to a smaller range and makes it comparable with other variables with different magnitudes which otherwise would not have been comparable. Min-max scaler (normalisation) restricts the data between 0 to +1.
This is an amazing video! I am trying to help my niece and have never read anything about KNN in my life but the way this video explains it is simply awesome! So thankful to you for creating this video as it would help thousands of students and those family members that want to help them learn properly. Do not understand why some professors can't seem to explain it so simply as you have! God bless you man!
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!
you put it better than I did. I have been struggling to understand what KNN means. Had over 6 lecturers mentioning it in the class but it still sounded vague. But he just made my day with this video.
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.
Hi, Simplilearn provides online training across the world. We would be happy to help you regarding this. Please visit us at www.simplilearn.com and drop us a query and we will get back to you! Thanks!
If a give an input list for the KNN algorithm to predict the classes of each element, How can I print out the list of inputs only belonging to a particular class?
We are glad you found our video helpful. 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!
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.
selecting the value for K is sqrt of number of data points but here in this exaple tutor has taken sqrt of number of data points in test sample, please explain...and also missed on different ways of selecting K values...
The video says one of the method to choose K value is sqrt(n) where n is number of Data Points, can anyone clarify data points here mean number of columns or volume of Data, if its volume then how to choose K value when volume is very high..?
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.
Hi, great video thanks for putting it up. I have a question - now we know the model is 80% accurate but how to apply this to the question if someone has diabetes or not? Thats what i want to know how to do. thanks.
"Hi Nima, If you have to choose the K value same as the size of the training set, then the values won't be segregated properly based on the nearest neighbors and the output will be very different."
hello, that's a great tutorial, thanks for the effort. I don't know if I missed it but I can't seem to find where you trained the model, or that step is not really important?!
Hi there, thanks for your video. it helps me alot in studing ML. I have one doubt, Why do you choose only glucose, BloodPressure, skinthickness, insulin and BMI?
Hi Joshi, thanks for checking out our tutorial and tossing out your queries. Of course, there are a lot of factors affecting the actual result but we have chosen the most important and obvious factors for a clear understanding of the concepts used and to not confuse the audience with medical jargons. Thank you.
I don't understand the confusion matrix. From what I understand, 94 - (actual) No diabetes 13 - (predicted) Has diabetes 32 - (actual) Has diabetes 15 - (predicted) No diabetes. Why is the matrix arranged like this? Wouldn't it be easier to compare actual vs predicted values for each outcome? Eg. 94 and 15 being in the same row so it translates to 94 people having no diabetes in actuality whereas the model predicted 15 people having no diabetes.
Hi Denise, Confusion matrix has 4 parameters - True positive, True negative, False positive and False negative. There are other metrics that can be derived from a confusion matrix. Watch the video below to learn how confusion matrix in detail: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-prWyZhcktn4.html Thank you for watching!
When the data is categorical then it is not very much recommended to use KNN as there are many other algorithms that would do the task easily. But, if you need to use KNN then you must convert all categories into numbers and assign inter-category distances.
Can you help me resolve this issue: NameError Traceback (most recent call last) in ----> 1 dataset = pd.read_csv('/Users/shudhhoroy/Documents/diabetes.csv') 2 print(len(dataset)) 3 print(dataset.head()) NameError: name 'pd' is not defined Edit: If anyone is having this issue I just forgot to upload the document to jupyter 😅
Is there a mistake at 7:54? It must be: (170-167)²-(57-54)². in your formular, the last number is 51... the x value of d1 in the coordinate system is wrong? Im right?
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Hi Sameer, we are not Edureka and you can check out this link to get certified in Machine Learning: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
Thank you for the nice and crystal clear explanation. I have one doubt over here in choosing the K value for given dataset. how it come to 11 ? As you taught 'K' Value should be sqrt of data points given which are 768.
Is there any way we can see what impact independent variables have on dependent variables like in linear regression? For example, if we were looking at the price of a car with price being dependent and mpg being independent, we can find that for every unit increase in mpg, a car's price goes up by $100. Is there a similar way to find the coefficients from our KNN model like we can with models like linear regression?
Hi The video is very nice about KNN. I have one question here: to find the optimum k value is it sqrt (y_test) or sqrt (y) sqrt (y_test)? Thanks in advance...
For choosing k, we are taking sqrt(n). what if the data points are 1000 the sqrt(1000) approx 31. Allocating k=31 is too much for the resources. Any other suggestions on this?
F1 score calculates the weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The formula is: F1 = 2 * (precision * recall) / (precision + recall)
Hello Murali, 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.
sir we have train the model which will predict the outcome ..but what will be procedure to predict the outcome if we are taking input from user and based on input from user we have to predict the output
You would having to save the model first, and then use this saved model to predict results on the user input. One thing you have to be careful is to preprocess the user input in the same way as the data is trained.
Taking input from the user will also follow the same process. After you have built you model using the training dataset, provide you own test dataset in a csv file to predict how well the is the model performing.
Here the p is not that we are predicting person is diabteic or not .. it is used becuase of euclidean distance formula..if we use manhattan, then p will be equal to 1
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In the real world if we get a list of 100 random people and based on our model we want to see if they have diabetes or not, how can we check that? Thanks.
Hi Muhammad, you can pass any random sample of data to any machine learning algorithm in the validation step. This will give you the predicted values of the random sample.
So if we put the value of K=7 and we have 1 million data point, then how will the algorithm decide to which 7 data points this new data point will be compared ?
U used test data to calculate k value. But we r fitting the model using train data. So, is it correct to use test data instead of train data for k value calculation. Plssss suggest..thx in advance.
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.
Glad you enjoyed our video! We have a ton more videos like this on ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-6M5VXKLf4D4.html. We hope you will join our community by subscribing to our channel!
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!
Hello Antonny, 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.
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Hi Abdul, thanks for watching our tutorial. We chose k=3 because we have n=9 and the square root of 9 = 3 in the given example. Hence, we have taken k=3. Hope that helps!
We are glad you found our video helpful, Muhammad. 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.
