# Reducing MAE or RMSE of linear regression

I'm trying to guess a home price, at final I intend to figure out a formula by using linear regression. As you can see over the url, I have 1480 data with 45 features in which price(fiyat) is the target variable.

Do high values for RMSE and MAE mean that the dataset cannot be trained in a good manner?

Is there a way to reduce the values? As you can see R2 seems well.

How can we say that how much percentage of error occurs for the guesses on average? If we can how much could you say?

There is really substantial difference between the prices and guesses as being seen below,

• This might be a related post: datascience.stackexchange.com/q/52398/71442 – Peter May 26 at 10:37
• @Peter are you sure that adding polynomial variables to "m2" reduces substantially the erroneousness? It even may increase. – concurrencyboy May 26 at 11:38
• It is a matter of try and error. However, beyond data augmentation, there is little room to improve OLS estimates. Mayber you can also try to "shrink" coefficients by L1/L2 norm (Lasso or Ridge). But I think in your case, this will not help too much. – Peter May 26 at 16:25

You can try things like outlier removal, reducing data skewness , stacking different models. For advanced regression guide checkout this kernel from kaggle : https://www.kaggle.com/serigne/stacked-regressions-top-4-on-leaderboard

Use below steps to get better results.

1. using describe function you will get know the values of each column if it contains numbers. find the outliers and replace those with Mean or Median or Mode values.
2. identify the columns to know the impact on data set ex: heat maps, we will get know the columns which are key once.
3. use multiple models(Linear Regression, Random forest,SVM and etc) with multiple parameters(change the parameter values in each model) for better results.
4. check the error with multiple models with multiple parameters and analysise the results.

Root Mean Square Error (RMSE) and Root Absolute Error (RAE) has same unit as the target value (home price in your case). It gives the mean error made by the model when doing the predictions of the given dataset. Depending on scale of your home price in training data it may not be that high. If scale of home price are in millions then the errors in thousands may not be that bad. Every dataset has some noise which causes inherent error on every model.

Still if it is high according to scale of home price in your dataset you may try some of following

• remove outliers data
• Do feature selection, some of features may not be as informative.
• Try to combine some features to make it more meaningful e.g. ratio of some feature may be more meaningful than individual features to predict price.
• May be the linear regression under fitting or over fitting the data you can check ROC curve and try to use more complex model like polynomial regression or regularization respectively

Before removing outliers or to "treat" the data long enough to suit your model, please have a look on the following article and check if a linear model is the best choice for your data set.

Please keep in mind that all those statistical models make assumptions about the data you give as an input. You will get only reliable results if those assumptions are met.