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Why does adding features to linear regression decrease accuracy?
If you are measuring accuracy in terms of the correlation between predicted output and actual output then the accuracy can decrease as you add more feature. linear regression does not guarantee anything about that. Thanks for contributing an answer to Stack Overflow!
Which is the loss function of a regression?
Let’s start by considering the most basic loss function which is nothing but the sum of errors in each iteration. The error will be the difference in the predicted value and the actual value. So the loss function will be given as: where the summation goes from n=1 to N where N is the number of instances in our dataset.
Why is mean squared error decreasing in regression?
Here, finally, comes in our warrior — Mean Squared Error. Its expression is: We take the average or mean of SSE. So more the data, lesser will be the aggregated error, MSE. Here as you can see, the error is decreasing as our algorithm is gaining more and more experience.
Which is better for regression loss Mae or MSE?
For MSE, gradient decreases as the loss gets close to its minima, making it more precise. Huber loss can be really helpful in such cases, as it curves around the minima which decreases the gradient. And it’s more robust to outliers than MSE. Therefore, it combines good properties from both MSE and MAE.
Why does the accuracy of the model decrease with increase in?
Knowing that increasing the feature number has strong impact on the performance of the algorithm. HTH. Ronaldo & Samer-The images are 150 by 150 tiled,from h&e stained images of high resolution. I am checking whether my algorithm generalises for a larger dataset to ensure that its not overfitting with the dataset1.
How to increase accuracy of machine learning model?
Multiple algorithms Hitting at the right machine learning algorithm is the ideal approach to achieve higher accuracy. But, it is easier said than done. This intuition comes with experience and incessant practice. Some algorithms are better suited to a particular type of data sets than others.
How to increase the accuracy of random forest classifier?
I totaly agree with @CrazyElf , switching to XGBOOST or other algo’s could increase the accuracy. then last but not least, missing values are preatty important (if they are a lot maybe there’s no need to drop them, but better consider them means to catch some insight) Thanks for contributing an answer to Data Science Stack Exchange!