Take this network: https://i.stack.imgur.com/nHnqk.png
How would I calculate
P(J|S∩E)
Is this correct?
P(J|S∩E) = P(J|S) * P(J|E) ?
I don't understand how they can be given since they are not connected directly.
Any suggestions would be greatly appreciated!
Solved it, I have to apply the Bayes Theorem with the additional condition.
https://dzone.com/storage/temp/8482917-screen-shot-2018-03-14-at-40747-pm.png
P(J|S∩E) = P(J∩S∩E)/P(S∩E)
Reference: https://dzone.com/articles/conditional-probability-and-bayes-theorem
and
https://stats.stackexchange.com/questions/176315/conditional-probability-of-event-a-given-events-b-and-c-occur-events-b-and-c-ar
Related
I have a dataset which has a fraud_label and some other sets of feature variable. How can I find the best rule which would help me identify fraud_label correctly with the best precision and recall values. Example of features are number_of_site_visits, external_fraud_score etc. I need to be able to come up with a rule which says that if number_of_site_visits is less than X and external_fraud_score is greater than Y then we will get the best precision and recall. I have to do this in Python and any help you can provide or direction would be very helpful.
I have tried Random Forest model but that gives me feature importances and not exact threshold values.
The best way to find the best rule for identifying fraud_label correctly with the best precision and recall values is to use a supervised machine learning algorithm such as logistic regression or support vector machines. These algorithms can be used to train a model on your dataset and then use the trained model to predict the fraud_label. The model can then be evaluated using metrics such as precision and recall.
You can also use grid search or cross-validation to find the optimal parameters for your model, which will help you identify the best thresholds for each feature variable. This will allow you to create a rule that will give you the best precision and recall values.
In Python, you can use scikit-learn library for implementing these algorithms.
I have been trying to share some contribution analysis with management using quantitative variable. However I am struggling to reconcile my y% increase to my x's. I have tried linear regression but dont think that will help immensely here. Please help...
Here is the data and below that is the template I need to submit
New to scikit-learn. I am using v 20.2. I am developing PLS regression models.I would like to know how important each of the original predictors/descriptors are in predicting the response. The diffferent matrices returned by scikit-learn for the learned PLS model(X_loadings, X_weights,etc) are giving descriptor-related values for each PLS component. But I am looking for a way to calculate/visualize the overall importance/contribution of each feature in the model. Can someone help me out here?
Also, what of the matrices shows me the coefficient assigned to each PLS component in the final linear model?
Thanks,
Yannick
The coef_ function of the model should give contribution of each descriptor to the response variable.
In my Theano program, I want to split the tensor matrix into two parts, with each of them making different contributions to the error function. Can anyone tell me whether automatic differentiation support this?
For example, for a tensor matrix variable M, I want to split it into M1=M[:300,] and M2=M[300:,], then the cost function is defined as 0.5* M1 * w + 0.8*M2*w. Is it still possible to get the gradient with T.grad(cost,w)?
Or more specifically, I want to construct an Autoencoder with different features having different weights in contribution to the total cost.
Thanks for anyone who answers my question.
Theano support this out of the box. You have nothing particular to do. If Theano don't support something in the crash, it should raise an error. But you won't have it for this, if there isn't problem in the way you call it. But the current pseudo-code should work.
I am running a random forest classifier using scikit's learn, and would like to calculate a precision metric (how many predictions matched the target value) as part of the results. Is there a built-in option to do that? If not what would be the easiest way to implement it?
Thanks!
Yes, see the reference documentation on performance metrics: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics