Quantile LINEAR regression by using Deep neural networks coded by Python
$10-30 CAD
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Posted over 2 years ago
$10-30 CAD
Paid on delivery
do it for all main Deep NN packages , but do not use hidden layers
LINEAR regression MEANS NO hidden layers
keras for example point to start [login to view URL]
tensor flow
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pytorch
then it should be 4 solutions, all of them should provide good performance
each solution in separate file
data is mixture of categorical and continues features
at least 3 categorical features
at least 3 continues features
at least 10000 rows
you find needed data sets by yourself : several datasets ( more than 2)
all data sets used for all models
see
primitive linear (or log-linear) model W \cdot \mathbf{x} + \mathbf{b} (where W is a weight matrix, \mathbf{x} = (x_1, x_2) is an input vector, and \mathbf{b} is a bias vector) without hidden layers,
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better to find and use existing code from web, then you include web links for source
to prove all done correctly: make prediction manually use only one dimension vector coefficients
like
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coef_ : ndarray of shape (n_features,) or (n_targets, n_features)
Weight vector(s).
you prove correctness by upper prediction = Data * coef_upper_
you prove correctness by lower prediction = Data * coef_lower_
coef_upper_
coef_lower_
like
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coef_ : array of shape (n_features,)
Estimated coefficients for the features
YOU NEED TO EXTRACT
coef_upper_
coef_lower_
FROM ML MODEL
you baseline FOR PREDICTOIN
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you baseline FOR UPPER AND LOWER INTERVALS
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and of course hyperparameters search like optimizer adam , mrop and etc
I'm interested in your project I've done something similar in last november where I worked on linear regression using neural network but not any hidden layer. hope mine work experience will help you.
Thank you!!