I am in learning phase of ML and i wanna know how to check for multicolinearity in Logistoc Regression? with codes and explanantion and pre-requiste to check for it? or any link will also do what is dummies pls cover that too as i was watching youtube they all were discussing? please over from scratch if possible please ?
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I want to know is there any link where I can find the scratch code of RANDOM FOREST REGRESSION in python? If yes please share the link with me.
Here's one: https://github.com/amstuta/random-forest
But you'll have better luck searching GitHub directly
I am new in Machine Learning and Deep Learning.
I am working on one use-case if any one can help pls.
We have employee attendance dataset i want to predict the employee in Time for the future days.
What algorithm i can use that will best fit to my problem. need some guidance how i can proceed.
this is basic machine learning, if you looked it up just a bit, you would have found a solution, with that being said, you could use linear regression
I'm trying to do a binary classification task on a set of sentences which are so similar to each other. My problem is I'm not sure how to deal with this problem with such similarity between samples. Here are some of my questions:
(1). Which classification technique will be more suitable in this case?
(2). Will feature selection help in this case?
(3). Could sequence classification algorithms, based on recurrent neural network (LSTM) be a potential approach to follow?
I'll be glad to see any hint or help regarding to this problem, thank you!
(only a potential Answer to 3)
Assuming you only have to classify if they are in a certain category you wouldn't want to use RNN's unless you actually want it to make something new out of it (sequence-to-sequence)
That said it is possible to classify it if you end it with a sequence-flattener and a fully-connected-Layer
Can anyone please help me in letting me know if there is any good library for doing anomaly detection using mahout?
Amongst other algorithms Mahout has an OnlineSummarizer which uses the T-Digest algorithm to compute online descriptive statistics. For an example of using The OnlineSummarizer for anomaly detection please see: Strata 2014-anomaly-detection.
I am working with OpenCV for a project used for recognition and I had a general question regarding the API and it's terms. I've looked online and couldn't find anything specific to this but I was wondering what the differences were regarding the Discrete Adaboost, Real AdaBoost, LogitBoost, and Gentle AdaBoost. If anyone could direct me to a pros v cons or a general description about these so that I may research which would be useful.
Update
I have added a link to a powerpoint file that goes over the different variations of the Boosting techniques. Hope this hopes someone else out there.
Adaboost powerpoint
Thanks in advance
There isn't really a simple "always use technique X" otherwise there wouldn't be a need for all the others . You really have to understand the details and experiment.
see The opencv discussion and A list of papers and technical summaries