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Another way to approach the problem is to take all of the trained models and compare each of their performances on the same hold-out dataset. This is the most common way to evaluate machine learning models. Choosing the evaluation metric to use depends on the goal of the project. Most machine learning projects care about predictive ability. R² is not a ...


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One interesting note, before proceeding: The $RMSE$ and $R^2$ values for your problem have a strong negative correlation. Just look at this graph with the first three values: The correlation coefficient is coming out to be -0.9999525 considering the first three models only. Now if, others are included, then also it stays the same. Now coming to your answer, ...


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The model result with higher R2, where the R2 score move towards one, the model performance improves. You must note that R2 score is a metric that tells the performance of your model, not the loss in an absolute sense that how well your model performed. In contrast, MAE, RMSE and MSE depend on the context whereas the R2 score is independent of context. For ...


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For starters you can find the correlation of each column with the output column and select the features which are highly correlated .This will also help you to remove features which will not contribute towards learning weights and biases . For example df.corr()["quality"] OUTPUT fixed acidity 0.119024 volatile acidity -0.395214 ...


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This is commonly called weak supervision, noisy, limited, or imprecise target values. One option is to train a surrogate model. Use the realtor prices as ground truth, then train a model that "translates" advertisement prices to mimic realtor prices.


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That problem would be better modeled as survival analysis, the expected duration of time until one event occurs. The event in your case would be revisit to the website. Survival analysis could also predict which people are most likely to revisit.


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Usually you should develop multiple models simultaneously. As the No Free Lunch Theorem states there is no way to know which model will perform better, before modeling. In practice you can make some educated guesses, but there is no need to rush them. If your output is continuous you shouldn't use a classification model like logistic regression. Although the ...


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To improve a model there are multiple things you can do, not just feature engineering or using different models. For example you haven't tried hyperparameter tuning for any of the above models. Here are some of the things you could try to improve the score (keep in mind that these are general tips for anyone who wants to improve their model):- 1.) First of ...


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There is no definite answer to this question. Usually all algorithms are tried and the best performing algo is selected. But to answer your question, it depends on the type of data you are working with and it's size. Below is a flowchart that guides you on what algo to choose for your dataset. (Keep in mind that the flowchart is to be taken with a pinch of ...


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The lambda parameter in ridge regression penalizes larger coefficients and pushes the model to balance the trade-off between fitting the data the best it can while taking into account the size of the coefficient. As a result coefficients are generally pushed closer to zero, which a larger amount of shrinkage for larger values of lambda.


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