I am using Boston Housing Data, I am using various model provided by keras and Sklearn to predict the house prices.
I want to know from an expert here on Stack Overflow, if I can use keras-rl or Re-inforcement learning to build a prediction model.
If so, how can i do so and integrate that in my NN code. I am looking over papers and kernels over kaggle and browsing, but unfortunately, could not find such implementation. The provided examples are solving games and balancing poles only, like playing games using RL.
TIA
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I am new to machine learning, but I have decent experience in python. I am faced with a problem: I need to find a machine learning model that would work well to predict the speed of a boat given current environmental and physical conditions. I have looked into Scikit-Learn, Pytorch, and Tensorflow, but I am having trouble finding information on what type of model I should use. I am almost certain that linear regression models would be useless for this task. I have been told that non-parametric regression models would be ideal for this, but I am unable to find many in the Scikit Library. Should I be trying to use regression models at all, or should I be looking more into Neural Networks? I'm open to any suggestions, thanks in advance.
I think multi-linear regression model would work well for your case. I am assuming that the input data is just a bunch of environmental parameters and you have a boat speed corresponding to that. For such problems, regression usually works well. I would not recommend you to use neural networks unless you have a lot of training data and the size of one input data is also quite big.
I am looking for classification models that are specified for brain image (neuro-scans) such as FDG-PET scans. I have a dataset that has images and the class assigned to each one of them (2 classes - Healthy patient, Diseased patient). I am heavily invested in the field of neuroscience and eager to seek new algorithms that could help analyze the images, however I am currently stranded on the island without any models left (and more importantly classifiers) to try out.
I have tried out Nilearn and FERM libraries but that's about it, received somewhat decent results (up to 75% CA with CV=20 on n=1000, where majority classification is 58%, there is an improvement). Is there any library that you could recommend to me to try out? This can be non-neuro oriented classification model classifiers as well.
The only requirement is that it's using supervised learning framework and that it's available as a Python library.
I am a beginner in JAX and I am trying to learn how to train a neural network. I saw some blogs, but as I understood there isn't a library that you can train it easily, like 'fit' as in sklearn. I am interested about classification task, could you please reccommend me any blogs in order to adopt his/her algorithm into my problem?
JAX is an array manipulation library, not a deep learning library: in that respect, you should think of it as more similar to NumPy than similar to scikit-learn. If you want neural networks built on JAX, there are several other good projects available such as haiku and flax.
I got one project in which I need to build a prediction model using linear regression in build. The case study is, need to predict winning of 5 candidates in an election. In this, I don't have any data and need to build data on my own but I am not able to visualize parameters. Can anybody help me in data building, it would be highly helpful.
you can start by get previous years election winner as your'e train data, if you don't have any train data , you have a problem in using linear regression (or supervised learning) after that if you want to use python try this step's :
use some code from this tutorial : https://machinelearningmastery.com/machine-learning-in-python-step-by-step/ or any other good beginner tutorial , you can join some comunity like https://www.kaggle.com/ and get some ideas from their kernel regarding to some processing of the data and tuning parameters
As I understand this question, you need to create a model based on data you don't have yet. Presumably you will get the data later on, by which time the model should already be implemented. You can create a fake data set using the numpy.random library. We's need more details on what exactly you're trying to do, though.
For a project I am working on, I am aiming to predict market trends and make long or short plays as a result. I am looking to use a reinforcement algorithm for this. In a paper I read recently however, the authors suggested using a two tiered system; an SVM classifier to determine market trend and three algorithms based on positive, negative or sideways market trend. Therefore, each algorithm is trained with data of the same trend so there exists less variability.
My question is, would using three algorithms improve the accuracy of the result, or would one model (with the same amount of data in total) provide the same accuracy?
Apologies if this seems a very basic question, I am new to machine learning and am eager to learn. Cheers
Different models have different strengths and weaknesses. This is the entire idea behind using an ensemble model.
What you can do is train a random forest or adaboost