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Train Accuracy : 95% - Test Accuracy : 35%

$30-250 CAD

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Publicado hace más de 5 años

$30-250 CAD

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Model_v2 is the code used to train a time series forecasting model. Actually, the training accuracy is quite good (i.e. 95%) and the testing accuracy is quite bad (i.e. 35). There's clearly the presence of overfitting. The purpose here is to improve the testing accuracy. I tried many things, but none of them worked well. I tried to increase the dropout ratio, increase the learning rate, increase the number of epochs, changed the number of input features. Many people think it is going to work with the right recipe. Suggestions : 1- Use weight decay (L2 regularization on the weights) 2- Try enforcing that all batches have the same number of -1, 0, 1 (i.e. classes) 3- "Setting a confidence threshold before accepting a prediction...so, unless buy or sell > 0.8, then do nothing"
ID del proyecto: 17314312

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Activo hace 6 años

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17 freelancers están ofertando un promedio de $155 CAD por este trabajo
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Hi dude, to solve that oveefitting problem you could try running cross validation to train the model :)
$100 CAD en 1 día
5,0 (68 comentarios)
6,5
6,5
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I have experience on Deep Learning I have experience on Deep Learning I have experience on Deep Learning
$155 CAD en 10 días
4,7 (9 comentarios)
3,7
3,7
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I have been working on machine learning models for the past one year. I caan help you with his using various techniques. Looking forward to have a chat with you
$250 CAD en 7 días
0,0 (0 comentarios)
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Hi.. I have completed a number of AI timeseries problem.. I can finetune your script with a couple of more parameters to increase the testing performance..
$133 CAD en 3 días
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Hi! I'm your statistician. Such a difference might be because the train set is not comprehensive and misses many values. I will find the reason why the difference and do my best to improve your test accuracy. Let me know when to start.
$222 CAD en 4 días
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Experienced Data Scientist with a demonstrated history of working in the telecommunications industry. Skilled in Python, C++, Machine Learning
$111 CAD en 5 días
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your overfit problem can be resolved in these ways: if the model is regularized, increase the dimensions of regularization parameters, for non-regularized models trying reducing the number of characteristics by their selection, or their extraction. Sometime happens that the model is fitting to much on the training data making it harder to generalize, and sometimes the problem can be resolved by adding more training data but maybe this is not the case. And otherwise trying optimizate the hyperparameters of the model. If you consider my proposal I can show you the accuracies I get.
$111 CAD en 5 días
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Good day, I am interested in working with you on this project you posted. I have developed my own system in analyzing Forex tick data that predicts whether or not the next price will be higher or lower than the current closing price. At the moment as it stands, it has a training accuracy of 99% and a test accuracy of 70% based on back testing EUR/USD tick data from all the way to 2010. If such a system seems to be something that can work for you, send me a message and we can discuss it in more detail. Kind regards, Justin van Zyl
$222 CAD en 3 días
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Hello. The problem you met is common but dont have the conventional solution. I met many of such situation and belive i can help you. Note the estimated time may vary due to size of you dataset and your architecture. I am mainly working with DL4j but also familiar with Keras.
$155 CAD en 5 días
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I'm trained as a financial engineer and I've worked extensively on time series methods. I've also worked with Recurrent Neural Networks based with Keras package based on tensorflow. I believe I can help you.
$155 CAD en 3 días
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Ste-Julie, Canada
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Miembro desde ago 1, 2017

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