Thanks for your query. I will try to give an illustration that may help you understand the difference between classification and prediction. Classification is a process when you want to identify between two or more objects, putting 'apples' and 'oranges' in two distinct baskets; i.e., separating objects into separate categories. This is a classification task. However, prediction can happen just with one object. For e.g. you want to predict what is the weather tomorrow (forecasting). Basically, based on the information at hand, you want to estimate an object, event, disease/pathogens and so on.
Dear Researcher, kindly guide, how can i cluster the questionnaire line items of the large data set. like more than1000 observation? I have 79 final line items of questionnaire now i want to cluster the line items into distinct latent variable. kindly guide me how can i cluster the line items. thanks in anticipation.
I don't understand your question very well. But it seems you have a dataset with 1000 samples (or more) and 79 features. If so, then many clustering techniques can be applied to find clusters of 1000+ samples. From very simple clustering techniques like the k-means algorithm to hierarchical clustering can be used. EM based clustering methods can also be used. Recenly, many clustering methods are developed in the field of single-cell analysis such as FEATS (pubmed.ncbi.nlm.nih.gov/33285568/) SC3, Seurat etc. These techniques can be used as well. If you know the number of clusters then your clustering problem would be easier otherwise some techniques like FEATS, Seurat etc also can find the best number of clusters.
While short, this video is fantastic. Very well thought out as evidenced by how compact it is - thank you very much for posting.
Thank you for your kind words
sir , your explanation is excellent . I m very impressed by your works. thank you sir .
Thank you for your kind words!
Very well explained. Thanks a lot sir 👍
Nice Explanation ....Thank You
Appreciate
good explained, thank you
can you make a simple video for function.RBF Classifier, and meta.classificationViaClustering algorithm in WEKA? how do they work?
Well Explained.. Thank you
Welcome 🙏
Nice 👍
Thanks
nicely explained :)
Thank you
Can you explean What the difference between classification and prediction??
Thanks for your query. I will try to give an illustration that may help you understand the difference between classification and prediction.
Classification is a process when you want to identify between two or more objects, putting 'apples' and 'oranges' in two distinct baskets; i.e., separating objects into separate categories. This is a classification task.
However, prediction can happen just with one object. For e.g. you want to predict what is the weather tomorrow (forecasting). Basically, based on the information at hand, you want to estimate an object, event, disease/pathogens and so on.
Good teaching
🙏
Dear Researcher, kindly guide, how can i cluster the questionnaire line items of the large data set. like more than1000 observation?
I have 79 final line items of questionnaire now i want to cluster the line items into distinct latent variable. kindly guide me how can i cluster the line items. thanks in anticipation.
I don't understand your question very well. But it seems you have a dataset with 1000 samples (or more) and 79 features. If so, then many clustering techniques can be applied to find clusters of 1000+ samples. From very simple clustering techniques like the k-means algorithm to hierarchical clustering can be used. EM based clustering methods can also be used. Recenly, many clustering methods are developed in the field of single-cell analysis such as FEATS (pubmed.ncbi.nlm.nih.gov/33285568/) SC3, Seurat etc. These techniques can be used as well. If you know the number of clusters then your clustering problem would be easier otherwise some techniques like FEATS, Seurat etc also can find the best number of clusters.
great
Thanks
Nice
Increase the speed to 1.75x then you are good to go!
thanks
sir , your explanation is excellent . I m very impressed by your works. thank you sir .