4

The problem you are portraying looks like a modified XOR problem. You can't throw away the lines with a label of 1 because a the model won't be able to learn this class.


2

Before jumping to modeling, there are a few tasks a data scientist (or ML/AI practitioner) must do: Ideation (or hypothesizing): Before applying any modeling approach, we need to ask the right questions. We must clearly mention our assumptions and declare how we want to measure the effectiveness of the pipeline. Note that, some tools/algorithms might not ...


2

Is your question about storing, writing, or reading/processing huge data? I'm not an expert in this topic, but I know a couple of possible ways to handle huge datasets: If the data is too big to be fully uploaded to RAM, you can iterate over it in Pandas. You can find a brief explanation in the article Why and How to Use Pandas with Large Data, section 1. ...


1

Just for clarification: your description (1 sample per minute) does not match the example data (far fewer data points which is understandable, but also two data points in one minute which contradicts the initial assertion.) If your actual measurements are like that you should first work on the sampling process to get reliable data. For creating predictions, ...


1

Thinking about this more, the answer is in fact yes, but not for the application you mention. You cannot use alpha-beta pruning to learn a model to predict customer outcomes, because it is only useful for domains where you are concerned about an adversary. In finding a customer model, there is no reason to worry about someone coming in and forcing you to ...


1

Yes, you can use sklearn's confusion_matrix. To explicitly extract the false positives and negatives, you can do from sklearn.metrics import confusion_matrix y_true = [0, 1, 0, 1] y_pred = [1, 1, 1, 0] tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()


1

This is perfectly acceptable in a stochastic environment. Generally your loss is to minimize $-log\ p(Y|X)$ or equivalently $-\sum_i log\ p(y_i|x_i)$. This optimization is equivalent to $-\mathbb{E}\log\ p(y_i|x_i)$. In other words you are minimizing in this case: $$ \begin{align*} L &= -log\ p(1|x_0) - log\ p(0|x_0) \\ &= -log [p(1|x_0) * p(0|...


1

You can use the function inverse_transform of the created MinMaxScaler object. See also this Stack Overflow question for other answers and examples.


1

Welcome to AI.SE @Par! What you have might be either a multi-label or a multi-class classification problem. If the classes are disjoint (each example belongs to just 1 of the 50 classes), it's a multi-class problem. If not (so each example can belong to several classes at once), it's a multilabel problem. Multi-label classification is usually handled by ...


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