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Community Bot
Community Bot
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The main problem here is that even before attempting to apply anomaly detection algorithms, you are not getting good enough predictions of gas consumption using neural networks.

If the main goal here is to reach the stage when anomaly detection algorithms could be used and you state that you have access to examples of successful application of linear regression for this problem, this approach could be more productive. One of the principles of successful machine learning application is that several different algorithms can be tried out before final selection based on results.

It you choose to tune your neural network performance, learning curvelearning curve plotting the effect of change in different hyperparameters on the error rate can be used. Hyperparameters that can be modified are:

  • number of features
  • order of the polynomial
  • regularization parameter
  • number of layers in the network

Best settings can be selected by the performance on cross validation set.

The main problem here is that even before attempting to apply anomaly detection algorithms, you are not getting good enough predictions of gas consumption using neural networks.

If the main goal here is to reach the stage when anomaly detection algorithms could be used and you state that you have access to examples of successful application of linear regression for this problem, this approach could be more productive. One of the principles of successful machine learning application is that several different algorithms can be tried out before final selection based on results.

It you choose to tune your neural network performance, learning curve plotting the effect of change in different hyperparameters on the error rate can be used. Hyperparameters that can be modified are:

  • number of features
  • order of the polynomial
  • regularization parameter
  • number of layers in the network

Best settings can be selected by the performance on cross validation set.

The main problem here is that even before attempting to apply anomaly detection algorithms, you are not getting good enough predictions of gas consumption using neural networks.

If the main goal here is to reach the stage when anomaly detection algorithms could be used and you state that you have access to examples of successful application of linear regression for this problem, this approach could be more productive. One of the principles of successful machine learning application is that several different algorithms can be tried out before final selection based on results.

It you choose to tune your neural network performance, learning curve plotting the effect of change in different hyperparameters on the error rate can be used. Hyperparameters that can be modified are:

  • number of features
  • order of the polynomial
  • regularization parameter
  • number of layers in the network

Best settings can be selected by the performance on cross validation set.

Improved formatting of the list.
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tomaskazemekas
tomaskazemekas
  • 313
  • 2
  • 13

The main problem here is that even before attempting to apply anomaly detection algorithms, you are not getting good enough predictions of gas consumption using neural networks.

If the main goal here is to reach the stage when anomaly detection algorithms could be used and you state that you have access to examples of successful application of linear regression for this problem, this approach could be more productive. One of the principles of successful machine learning application is that several different algorithms can be tried out before final selection based on results.

It you choose to tune your neural network performance, learning curve plotting the effect of change in different hyperparameters on the error rate can be used. The number of features, the order of the polynomial features, regularization parameter or number of layers in the networkHyperparameters that can be modified andare:

  • number of features
  • order of the polynomial
  • regularization parameter
  • number of layers in the network

Best settings can be selected by the best performance on cross validation set.

The main problem here is that even before attempting to apply anomaly detection algorithms, you are not getting good enough predictions of gas consumption using neural networks.

If the main goal here is to reach the stage when anomaly detection algorithms could be used and you state that you have access to examples of successful application of linear regression for this problem, this approach could be more productive. One of the principles of successful machine learning application is that several different algorithms can be tried out before final selection based on results.

It you choose to tune your neural network performance, learning curve plotting the effect of change in different hyperparameters on the error rate can be used. The number of features, the order of the polynomial features, regularization parameter or number of layers in the network can be modified and selected by the best performance on cross validation set.

The main problem here is that even before attempting to apply anomaly detection algorithms, you are not getting good enough predictions of gas consumption using neural networks.

If the main goal here is to reach the stage when anomaly detection algorithms could be used and you state that you have access to examples of successful application of linear regression for this problem, this approach could be more productive. One of the principles of successful machine learning application is that several different algorithms can be tried out before final selection based on results.

It you choose to tune your neural network performance, learning curve plotting the effect of change in different hyperparameters on the error rate can be used. Hyperparameters that can be modified are:

  • number of features
  • order of the polynomial
  • regularization parameter
  • number of layers in the network

Best settings can be selected by the performance on cross validation set.

Source Link
tomaskazemekas
tomaskazemekas
  • 313
  • 2
  • 13

The main problem here is that even before attempting to apply anomaly detection algorithms, you are not getting good enough predictions of gas consumption using neural networks.

If the main goal here is to reach the stage when anomaly detection algorithms could be used and you state that you have access to examples of successful application of linear regression for this problem, this approach could be more productive. One of the principles of successful machine learning application is that several different algorithms can be tried out before final selection based on results.

It you choose to tune your neural network performance, learning curve plotting the effect of change in different hyperparameters on the error rate can be used. The number of features, the order of the polynomial features, regularization parameter or number of layers in the network can be modified and selected by the best performance on cross validation set.