New answers tagged predictive-modeling
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How to cluster based on sensor data? - My first data science job
First of all: Congratulations to your job!
It looks to me as if you have two challenges at hand:
Creating a good clustering on time series from multiple sensors
Interpreting the clustering and ...
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How can i implement an confucion matrix?
Confusion matrices are supported by scikit-learn (see also lya Lees answer).
In cell 1, you can import it via
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Get the Polynomial Equation with Two Variables in Python
Your dependant variable (price) needs to be on the Y-axis and your independent variable (length) needs to be on the X-axis. The resulting equation (if polynomial) will then output price when you enter ...
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How can someone build a dataset for a "propensity to purchase" model?
This is a long post with many questions, but I will try my best to answer.
Let's start with the terminology: when we say "propensity model", usually we mean predicting future events (e.g. ...
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Accepted
Do models of social systems suffer from prediction drift?
I wouldn't call it normal but it surely is possible. There are several reasons:
Changes in fraud patterns: If the model was trained on historical data that is no longer representative of current ...
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How can i implement an confucion matrix?
Without having read too much your code, a confusion matrix states how many elements from class $y_1, y_2, y_3..$ have been associated by the model to class $y_1, y_2, y_3..$
So, for n classes, the ...
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Removing constant from the regression model
The regression constant is an output part. You should not ignore it. Further, your interpretation of summary outputs is invalid. The regression coefficient of the independent variable is highly ...
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Scaling and handling highly correlated features in tabular data for regression
PV3 is highly correlated with a number of other variables. It can be dropped to avoid multiple interactions.pv6 has positive and negative correlations. It is desirable to remove it in the light of ...
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What to do if your model's prediciton result wrong because of unlucky?
All models are wrong, some are useful. The most useful ones are the ones constructed by people who understand the probabilities of things happening, even "black swan" events (things everyone ...
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How a Random forest "learns" or How loss (objective function value) is propagated back so that a random forest can "Improve"?
In a random forest classifier, there is no backpropagated loss. Instead, the N trees are grown independently from each other and then, for a new prediction, a ...
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