logo
down
shadow

MACHINE-LEARNING QUESTIONS

Weka IBk parameter details (distanceWeighting, meanSquared)
Weka IBk parameter details (distanceWeighting, meanSquared)
This might help you If one uses "no distance weighting", then the predicted value for your data points is the average of all k neighbors. For example
TAG : machine-learning
Date : November 21 2020, 09:01 AM , By : pepkas
Accuracy rate for kNN classification dropped after feature normalization?
Accuracy rate for kNN classification dropped after feature normalization?
hope this fix your issue It is a general misconception that normalization will never reduce classification accuracy. It very well can.HOW ?
TAG : machine-learning
Date : November 14 2020, 06:58 AM , By : ClumsyPixel
Which Machine Learning technique is most valid in this scenario?
Which Machine Learning technique is most valid in this scenario?
it should still fix some issue You have a multi-class classification problem with 1728 samples. The features are in 6 groups:
TAG : machine-learning
Date : November 11 2020, 09:01 AM , By : user3525475
Gradient descent for more than 2 theta values
Gradient descent for more than 2 theta values
like below fixes the issue Gradient descent algorithm is given as : , Maybe convert theta to matrix notation then
TAG : machine-learning
Date : November 08 2020, 09:00 AM , By : FyreFeonix
Under what parameters are SVC and LinearSVC in scikit-learn equivalent?
Under what parameters are SVC and LinearSVC in scikit-learn equivalent?
I think the issue was by ths following , I read this thread about the difference between SVC() and LinearSVC() in scikit-learn. , In mathematical sense you need to set:
TAG : machine-learning
Date : November 07 2020, 01:32 PM , By : Milad Tehrany
what does pos_label in f1_score really mean?
what does pos_label in f1_score really mean?
it should still fix some issue The f1 score is the harmonic mean of precision and recall. As such, you need to compute precision and recall to compute the f1-score. Both these measures are computed in reference to "true positives" (positive instances
TAG : machine-learning
Date : November 04 2020, 09:01 AM , By : Bruce
How to check if gradient descent with multiple variables converged correctly?
How to check if gradient descent with multiple variables converged correctly?
this one helps. Gradient descent converges to a local minimum, meaning that the first derivative should be zero and the second non-positive. Checking these two matrices will tell you if the algorithm has converged.
TAG : machine-learning
Date : November 01 2020, 09:01 AM , By : KOMAL SRIVASTAVA
Principal component analysis vs feature removal
Principal component analysis vs feature removal
hop of those help? There is a fundamental difference between feature reduction (such as PCA) and feature selection (which you describe). The crucial difference is that feature reduction (PCA) maps your data to lower dimensional through some projectio
TAG : machine-learning
Date : October 31 2020, 01:49 PM , By : SanjayH
Error of output neuron
Error of output neuron
With these it helps Forget about all these trendy names as back propagation, it's nothing more than simple taks of mathematical optimization. One of the possible ways to optimize cost function - use gradient descend iterative algorithm, to use it you
TAG : machine-learning
Date : October 31 2020, 05:57 AM , By : Asynchronous Paradox
Vowpal Wabbit doesn't save the model despite -f flag is present
Vowpal Wabbit doesn't save the model despite -f flag is present
Any of those help That was a bug in Vowpal Wabbit source code. Now it's fixed and models are saved as expected. Here is an issue on Github: https://github.com/JohnLangford/vowpal_wabbit/issues/859
TAG : machine-learning
Date : October 28 2020, 05:11 PM , By : Janrolan Dalusong
shadow
Privacy Policy - Terms - Contact Us © animezone.co