4

Laplace smoothing is a way to move probabilities towards uninformed mean. Suppose you have a multinomial variable with sample counts $c_1, c_2,..,c_d$, where $d$ is the number of dimensions. A Laplace smoothed version of estimated probabilities has the form: $(c_i + \alpha)/(N + d\alpha)$, where $\alpha$ is positive. If $\alpha$ is $0$ then we have non ...


2

The fact that you are bringing up the issue of balance means that you have not considered the fact that proportion "classified" "correctly" is a discontinuous improper accuracy scoring rule. If you use a proper scoring rule (e.g., Brier score or pseudo $R^2$) the issue goes away. See this and this for more.


2

If the impetus behind your two models was two different subpopulations, then it sounds like the most natural course of action would be to apply the appropriate model to each sample, depending on what subpopulation the sample belongs to. No combination required. If you do want to combine, I don't see a clear reason to prefer one over the other of your ...


2

The reason behind using the weights this way was to try to keep the model away from instances with very high/very low probability. My interpretation is that the model is fairly confident about those instances and hence should focus elsewhere. This is similar to AdaBoost, giving higher weights (during fitting, updating weights between trees) to misclassified ...


1

Add another RNN. Your first RNN outputs a set of features for each sentence, based on words or characters. (I used a CNN myself.) Your second (bi-directional) RNN takes those features and outputs features based on the features of the current and other sentences. Your fully-connected classifier then assigns a classification to each line. You’ll need to ...


1

I'm not sure "the objective function of XGBoost is 'binary:logistic', the probabilities should be well calibrated" is correct: gradient boosting tends to push probability toward 0 and 1. Furthermore, you're applying weights, which should also skew your probabilities. Because gradient boosting pushes probabilities outward rather than inward, using Platt ...


1

Kmeans Clustering is a Cluster Algorithm to separate your data into different clusters. Of course you can assign each cluster a representing class. When you got an additional unknow point you calculate the distance to the nearest centroid (cluster center) and add this point to this cluster/class. What does it mean is, that you would use for this ...


1

Stephan Kolassa already gave a great answer. I'd like to add some thoughts regarding the stratification. I can't stress enough that you must split data based on users (train/test/val), if you intend to evaluate performance for a new user. If you leak data of the same user between your different sets it will give you biased performance estimates.


Only top voted, non community-wiki answers of a minimum length are eligible