# Tag Info

### Linear regression assumptions

To some extent, I disagree with every one of these. Nonlinear regression models (e.g., SVM) do not assume a linear functional form, and nonlinear basis functions (e.g., polynomials or splines) can ...

### Calculate RMSE based on R squared and vice versa

$$R^2 = 1 - \dfrac{(RMSE)^2} { \sigma^2_y }$$ (Use the “population” variance calculation for $\sigma^2_y$.) You have to have the denominator term in order to convert between $R^2$ and $RMSE$, but ...

### Linear regression returning negative values for house price prediction

This can happen with regression, especially if the training data is too small and/or the test data has important differences with the training data. It can be caused by bias or overfitting, but it's ...
1 vote

### Why can Random Forest "handle missing values and cardinality well compared to linear regression"?

Generally, random forests are a much more sophisticated method than linear regression: it's an ensemble method with multiple decision trees, and a single decision tree is already a much more flexible ...

### Why do we don't write units with MAE or RSME for regression problem ? If I wish to write the units when how do I identify the units for them?

RMSE and MAE do have units. In fact, they have the same units. (Determining why that’s the case is a useful exercise.) You determine those units by considering the units of your $y$. If $y$ is ...

### Why do we don't write units with MAE or RSME for regression problem ? If I wish to write the units when how do I identify the units for them?

In general evaluation measures don't have any unit. Many of them actually represent proportions of instances (like accuracy, precision, recall). But it's true that error evaluation measures are ...

### Training data in sentiment analysis

Depending on the language of the tweets you collected, and the availability of pre-trained sentiment analysis models for this language. You should aim for models trained on the most similar domains, ...
1 vote

### Get result from log transformed variable

Exponentiation is the inverse function of logarithms. You can return to the original sale price like this: $orignal = B^{x}$ where $B$ is the logarithm base and $x$ is the log-transformed sale price. ...
1 vote

### What is the right way of training Regression model having various categories involved?

The question of the role of this 'category' column matters: If the categories are independent, i.e. instances in category X have no relation with instances in category Y, then it makes more sense to ...

Top 50 recent answers are included