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Results tagged with machine-learning
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user 238499
Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.
3
votes
Accepted
Calculate AUC based on TPR and FPR
There's nothing more practical than a good theory! You need to graph TPR against FPR (for example, for $t=0.1$, $TPR=0.81$ and $FPR=0.98$, this gives you one point of the graph). The image below takes …
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0
votes
Accepted
Should I scale targets when building regression model with multiple objects?
I don't think it's necessary. Neural networks don't need your target to be a standardized normal variable. Don't hesitate to share your results though!
- 2,665
1
vote
Accepted
What is after doing k-fold cross-validation?
The main goals behind K-fold cross validation are
Selecting one model among many with an objective criterai that relates to the model's usefulness.
Have a first idea on how the model will perform.
…
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-1
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How to Recursively Predict a Time Series Using Neural Networks
Well, you are right about the way to make recursive predictions. Maybe there's something wrong with the type of model you've fitted.
Neural networks are very complex models (thus, overfitting machine …
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1
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Why to choose AUC over accuracy?
Accuracy is a legitimate validation metric when you are working with a balanced dataset. However, it is often the case, in classification problems, that there is a clearly majority-class. Also, errors …
- 2,665
1
vote
Fitting model again after variable selection
You can fit again the model considering only the 10 selected features but I think that, whenever possible, you should shuffle the train and test sets again, fit the "simple" model with the new train d …
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1
vote
Does the MSE values of regression coefficients sum up to the MSE value of the regression mod...
You can easily check with an example that that is not the case, even though they are somehow related (with all other things being equal, the larger the coefficient estimation error, the larger the mea …
- 2,665
0
votes
Accepted
vector support machine - margin?
$\vec{x}$ is here a variable. Each of the two equation holds true for a certain set of points. If it does for $\vec{x}$, then $\vec{x}$ is one of the points on the border.
I think normalization is a …
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1
vote
The most appropriate model for dataset
That's the magic of data science. There is no way to know a priori!
It is true that you cannot see everything in one simple visualization with more than 2 variables, but you can find some workaround. …
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0
votes
Overfitting and Underfitting
In a nutshell, overfiitting appears as a consequence of patterns that appear in your training dataset but are not present on the entire population (they appeared out of luck) If your use a simple mode …
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2
votes
Median Absolute Deviation
Please remember that R^2 and MAD are measures for two completelly different things. R^2 measures correlation between two variables, while MAD deals with the dispersion of a single variable.
The reaso …
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1
vote
Machine learning for inequalities
Regression models are often capable of calculating standard deviations for their predictions. You could then build a confidence interval.
In simpler problems like linear regression, this problem is q …
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0
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Interpretation of model
It seems your models are pretty much equally efficient, so maybe all four features are relevant. Have you tried a model that incorportaes all of them?
- 2,665
0
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Function with multiple local minima
If they are only a few and you can estimate a range where they will lay, you can try descent methods with different starting points that will converge to each of them.
This practice works some of the …
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0
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Understanding which features were most important for logistic regression
You are right about why you should not use the coefficients as a measure of relevance, but you absolutelly can if you divide them by their standard error! If you have estimated the model with R, then …
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