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
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I'm working for a project and I have a doubt. I'm really new to data mining world, and Supervised and Unsupervised Learning.
I'm currently doing a work in Unsupervised Learning, and I'm trying to understand if the "Imbalaced Learning" methods are used in unsupervised learning problems or if it's only used in supervised ones.
The work is in python, so I've looked for python SMOTE method to deal with imbalanced learning, but it always appear "X_train and y_train", and since unsupervised learning don't have a target, I don't know how would i use SMOTE in this case.
The question seems simple, can we find a neural network for every classical Machine Learning model?
For example:
Linear regression is a perceptron.
PCA is an auto-encoder with a single intermediate layer
We can approximate Ridge or Lasso by adding some decay at the time of the construction of the network
If the answer to the first question is yes then how can I find equivalents to decision trees and SVMs?
What Machine Learning Method should i Use to predict Prices like Stocks,gold and etc?
I Prefer using Python but I Can't Find the Starting Point as it Seems so Complicated to me and I've no Clue How to Start it.
Talking about the machine learning method, Regression Method is used for Price prediction as it is used to predict a continuous variable. There are wide range of techniques for regression in machine learning. Starting from simple linear regression, SVR, RandomForest, CatBoost to RNN. Based on target problem, available datasets and computing resources, one of the algorithms can be used.
Yes, Python is the best language to get started into machinbre learning. And definitely, Linear Regression is the best way to start for this regression task if you are new. Gradually, you can start exploring other techniques in scikit-learn before directly jumping into RNN. Scikit-learn is the best machine learning library from beginners to professionals.
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.
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.