Does WEKA have Bayesian linear regression implementation?
Microsoft Azure has Bayesian linear regression https://msdn.microsoft.com/en-us/library/azure/dn906022.aspx which in my case performs better that linear regression of WEKA.
NO, I as per the weka documentation, it has only Bayesian Logistic Regression. I suggest using linear regression classifier only with weka.
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I am using scikit-learn to build a multiclass classification model. To this extent, I have tried the RandomForestClassifier, KNeighborsClassifier, LogisticRegression, MultinomialNB, and SVC algorithms. I am satisfied with the generated output. However, I do have a question about the default mechanism used by the algorithms for multiclass classification. I read that all scikit-learn classifiers are capable of multiclass classification, but I could not find any information about the default mechanism used by the algorithms.
One-vs-the-rest or One-vs-all is the most commonly used and a fair default strategy for multiclass classification algorithms. For each classifier, the class is fitted against all the other classes. Check here for more information https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html
would love to hear your thoughts on a topic. I am trying to evaluate the performance of multiple/ensemble binary classifiers for multi-class classification vs a single CNN for multiclass classifications. The goal is object classification using images as input. Looking to optimize on true positives, compute efficiency and network complexity
Where and why is linear and non-linear transformations is useful. What are use cases in Machine learning and in deep learning especially for computer vision.
A neural net can be seen as framework for linear transformation.
Think of the math operations which are defining the linear transformation and compare it with a neural net.
We were in a meeting discussing hiring a consultant we trust to do machine learning. On colleague piped up that it was just logistic regression and that he would do it
What is machine learning and how does it relate to logistic regression
Logistic regression is a supervised machine learning strategy. It classifies input data into two classes
If I were to train an artificial neural network's weights using a genetic algorithm what type of learning would this be classed as? I believe it's unsupervised but does it have a name? It seems like it could fall under reinforcement learning but I'm not too sure.
Genetic algorithm itself is an optimization algorithm rather than a learning algorithm. And you probably don't want to ignore the performance of neural network and only consider the weight changes. So what type of learning is a combination of neural network and genetic algorithm depends on the learning type of neural network. A neural network can be used for supervised learning, reinforcement learning, and even unsupervised learning.
It seems such a combination applies more in reinforcement, because genetic algorithm is slower than most backpropagation-based optimization algorithms with gradient information. Updating the weights with genetic algorithm is also called neuroevolution. This post cited many research on neuroevolution over traditional neural network in case you may be interested in.
It is also possible to apply genetic algorithm to unsupervised neural network. In Shibata et al's Nonlinear backlash compensation using recurrent neural network. Unsupervised learning by genetic algorithm, the authors applied the genetic algorithm to determine the weights of the recurrent neural networks, and the approach does not need the teaching signals.