The default value for Keras model.compile metrics parameter is metrics=None. There are a plenty of explanations and information about this parameter different values, and I believe I pretty much understand their meaning and purpose, but what I struggle finding, is what is the behavior of the default value metrics=None.
The official documentation of the model.compile method here doesn't say anything about the default value (which is None), and googling about it for a while hasn't brought any enlightenment for me either so far.
I'd be greatful for any helpful hint on this!
It means it will not calculate any metric, it is not necessary to specifiy a metric. The only parameters that need to be specified are optimizer and loss.
These are the only things necessary to train a model, the metric doesn't go into the calculation for backpropagation.
By not specifing metric, simply no metric will be calculated. A metric is something extra that is calculated for you to simbolize how well a problem is being solved. Usually for classification you would use cross entropy.
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I have several questions regarding the ETS model in statsmodels library. The description of the model can be found here.
The default initialization_method is estimated. In the description, it says ‘estimated’ uses the same heuristic as initial guesses, but then estimates the initial states as part of the fitting process. What does this mean? What is the heuristic value as the initial guesses? How does the estimation work? Does it try to minimize the sum of squared errors of the one-step ahead forecast?
If I specify damped_trend = True, how does the model choose the optimal damping parameter?
Couldn't find a precise and concise answer. I'm not particularly interested in different machine learning evaluation methods, I just want to know why it's important to have more than one?
Each metrics gives a different insight and evaluates your model differently.
Let's take an example for binary classification:
Accuracy tells you what percentage of your predictions are correct. But what if you also want to know exactly how many 1's you got wrong [i.e. you predicted 0's where they should be 1]. for this, you will calculate the recall score.
So you get the idea maybe you want good accuracy but also good recall [real world example : maybe spam detection], so you look at both metric and choose wisely
I have always been using r2 score metrics. I know there are several evaluation metrics out there i have read several articles about it. Since i'm still a beginner in machine learning. I'm still very confused of
When to use each of it, is depending on our case, if yes please give me example
I read this article and it said, r2 score is not straightforward, we need other stuff to measure the performance of our model. Does it mean we need more than 1 evaluation metrics in order to get better insight of our model performance?
Is it recommended if we only measure our model performance by just one evaluation metrics?
From this article it said knowing the distribution of our data and our business goal helps us to understand choose appropriate metrics. What does it mean by that?
How to know for each metrics that the model is 'good' enough?
There are different evaluation metrics for regression problems like below.
Mean Squared Error(MSE)
Root-Mean-Squared-Error(RMSE)
Mean-Absolute-Error(MAE)
R² or Coefficient of Determination
Mean Square Percentage Error (MSPE)
so on so forth..
As you mentioned you need to use them based on your problem type, what you want to measure and the distribution of your data.
To do this, you need to understand how these metrics evaluate the model. You can check the definitions and pros/cons of evaluation metrics from this nice blog post.
R² shows what variation of your purpose variable is described by independent variables. A good model can give R² score close to 1.0 but it does not mean it should be. Models which have low R² can also give low MSE score. So to ensure your predictive power of your model it is better to use MSE, RMSE or other metrics besides the R².
No. You can use multiple evaluation metrics. The important thing is if you compare two models, you need to use same test dataset and the same evaluation metrics.
For example, if you want to penalize your bad predictions too much, you can use MSE evaluation metric because it basically measures the average squared error of our predictions or if your data have too much outlier MSE give too much penalty to this examples.
The good model definition changes based on your problem complexity. For example if you train a model which predicts that heads or tails and gives %49 accuracy it is not good enough because the baseline of this problem is %50. But for any other problem, %49 accuracy may enough for your problem. So in a summary, it depends on your problem and you need to define or think that human(baseline) threshold.
Do you know how to intepret RAE and RSE values? I know a COD closer to 1 is a good sign. Does this indicate that boosted decision tree regression is best?
RAE and RSE closer to 0 is a good sign...you want error to be as low as possible. See this article for more information on evaluating your model. From that page:
The term "error" here represents the difference between the predicted value and the true value. The absolute value or the square of this difference are usually computed to capture the total magnitude of error across all instances, as the difference between the predicted and true value could be negative in some cases. The error metrics measure the predictive performance of a regression model in terms of the mean deviation of its predictions from the true values. Lower error values mean the model is more accurate in making predictions. An overall error metric of 0 means that the model fits the data perfectly.
Yes, with your current results, the boosted decision tree performs best. I don't know the details of your work well enough to determine if that is good enough. It honestly may be. But if you determine it's not, you can also tweak the input parameters in your "Boosted Decision Tree Regression" module to try to get even better results. The "ParameterSweep" module can help with that by trying many different input parameters for you and you specify the parameter that you want to optimize for (such as your RAE, RSE, or COD referenced in your question). See this article for a brief description. Hope this helps.
P.S. I'm glad that you're looking into the black carbon levels in Westeros...I'm sure Cersei doesn't even care.
I am using scikit-learn's LogisticRegression object for regularized binary classification. I've read the documentation on intercept_scaling but I don't understand how to choose this value intelligently.
The datasets look like this:
10-20 features, 300-500 replicates
Highly non-Gaussian, in fact most observations are zeros
The output classes are not necessarily equally likely. In some cases they are almost 50/50, in other cases they are more like 90/10.
Typically C=0.001 gives good cross-validated results.
The documentation contains warnings that the intercept itself is subject to regularization, like every other feature, and that intercept_scaling can be used to address this. But how should I choose this value? One simple answer is to explore many possible combinations of C and intercept_scaling and choose the parameters that give the best performance. But this parameter search will take quite a while and I'd like to avoid that if possible.
Ideally, I would like to use the intercept to control the distribution of output predictions. That is, I would like to ensure that the probability that the classifier predicts "class 1" on the training set is equal to the proportion of "class 1" data in the training set. I know that this is the case under certain circumstances, but this is not the case in my data. I don't know if it's due to the regularization or to the non-Gaussian nature of the input data.
Thanks for any suggestions!
While you tried oversampling the positive class by setting class_weight="auto"? That effectively oversamples the underrepresented classes and undersamples the majority class.
(The current stable docs are a bit confusing since they seem to have been copy-pasted from SVC and not edited for LR; that's just changed in the bleeding edge version.)