Questions tagged [rmse]

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Calculate RMSE based on R squared and vice versa

If for example I have the value of RMSE can I calculate the $R^2$? And vice versa if I have the value of $R^2$ can I calculate the value of RMSE? I have all predictions, dataset, training set, and ...
1 vote
1 answer
91 views

Linear regression returning negative values for house price prediction

I am trying to do a prediction of real estate (prices are in millions). The mean price for the dataset is 4 million. I do not have any negative values in my dataset,...
1 vote
1 answer
40 views

My Linear Regression Model Mean Absolute Error(MAE) is 0.29 and R2 0.20 , Is this a acceptable Model?

My Linear Regression Model Mean Absolute Error(MAE) is 0.29 and R2 0.20 , Is this a acceptable Model ? How can increase the r2 score ?
0 votes
1 answer
32 views

Why does log-transforming the target have a huge impact on MSE value?

I am doing linear regression using the Boston Housing data set, and the effect of applying $\log(y)$ has a huge impact on the MSE. Failing to do it gives MSE=34.94 ...
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2 votes
3 answers
88 views

Measuring performance of customer purchase predictions

My goal is to develop a model that predicts next customer purchases in USD (Update: During the time period of the dataset, if no purchase was made by the customer, the next purchase label is set to ...
0 votes
0 answers
33 views

High loss but low rmse, how?

I have trained an lstm model on a dataset but its loss during training is ten times than the rmse during test. How is it possible, and can I use this model if rmse is very low but loss is high? How ...
0 votes
1 answer
384 views

What does rmse of a LSTM model tells?

Suppose I made a model which has rmse of 50 Now when I predict the next data which is 500 So does that mean the actual value has high probability to be within the range of 450 - 550 ? If so what is ...
1 vote
1 answer
1k views

How could we interpret a SI Scatter Index and RMSE?

SI is RMSE divided by the average value of the observed values (or the predicted values? am confused)? is SI = 25% acceptable? (is the model good enough? )
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0 votes
0 answers
16 views

Appropriate loss function and metrics for regression task with mixed outputs

I'm trying to train an EfficientNet-based Keras model that takes an image as input and returns two numeric values as output. Here's the model: ...
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1 vote
1 answer
29 views

Difference in result in every run of Neural network?

I have written a simple neural network (MLP Regressor), to fit simple data frame columns. To have an optimum architecture, I also defined it as a function to see whether it is converging to a pattern. ...
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1 vote
3 answers
115 views

Determining which model result is better

I am trying to determine which model result is better. Both results are trying to achieve the same objective, the only difference is the exact data that is being used. I used ...
0 votes
1 answer
173 views

How to reduce RMS error value in regression analysis & predictions - feature engineering, model selection

There's this dataset containing the metadata of Twitch's top 1,000 streamers of 2020. You can have the details here. I am currently participating in a challenge to predict the values for Followers ...
0 votes
1 answer
32 views

Comparing RMSEs of multiple test sets having different sizes

The data I have is a time series data (stock returns), and I am training a Random Forest Regressor on it. Total observations = 2499 To better evaluate the performance, I have implemented rolling ...
0 votes
1 answer
181 views

Feature engineering: The more features I add the better RMSE I get?

I have a model with 7 features, I'm trying to figure out if I can improve the performance of this model by adding additional features. So I'm relying on the RMSE to measure the accuracy of my ...
0 votes
1 answer
35 views

Why is linear regression not doing worse with a low weighted attribute?

I've been able to build a few linear regression models that can predict a material strength quite well: minimum RMSE of 17.95 using 11 attributes that I have selected from 159 original attributes. The ...
2 votes
1 answer
47 views

What is bad, good and excellent metric score for time series model?

I have created a couple of models for my master project and I used several metrics for evaluation. I used MSE, MAE, MAPE, RMSE not because I really learned about them a lot, because I saw in many ...
2 votes
0 answers
351 views

How to add RMSE value on a plot with ggplot [closed]

I added r2 value and the formula of the regression function but I also want RMSE value on my plot, maybe I need to add something but I could not see a proper answer to this question neither here nor ...
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1 vote
1 answer
373 views

How to interpret the Mean squared error value in a regression model?

I'm working on a simple linear regression model to predict 'Label' based on 'feature'. The two variables seems to be highly correlate corr=0.99. After splitting the data sample for to training and ...
0 votes
1 answer
348 views

How many features do I select when doing feature selection for regression algorithms? Is R2 and RMSE good measures of success for overfitting?

Context: I'm currently crafting and comparing machine learning models to predict housing data. I have around 32000 data points, 42 features, and I'm predicting housing price. I'm comparing Random ...
2 votes
2 answers
240 views

RMSE is higher for bigger values of target variable - how to decrease

I am solving a problem with machine learning and I have some data with two integer type independent variables and a continuous dependent variable. I am optimising to RMSE. I had fairly large RMSE ...
1 vote
1 answer
490 views

How do you identify whether your RMSE score is good or not?

Im building a XGBoost regression model to predict the values in the range of -3 to 3. Im using Root Mean Squared Error to evaluate the model. With hyper-parameter tuning and everything the best scores ...
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1 vote
1 answer
357 views

SVR - RMSE is much worse after normalizing the data

I'm building a model using a custom kernel SVR that looks into a few of my dataframe's features and checks the proximity/distance between each pair of datapoints. The features are weigthed and the ...
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1 vote
1 answer
166 views

Which error metric is good for measuring accuracy

I am estimating water depth with satellite data (predicted value) and would like to validate my result using bathymetry lidar data collected on the field and believed to be more accurate (observed ...
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0 votes
1 answer
65 views

Perform bootstrapping of an ordinary linear regression model, using B=100 bootstrap resamples of my dataset, and getting RMSE

So Im studying machine learning through R, and Im working with the boston data set from the library MASS. I am practicing bootsrapping. I already carried out analysis to determine how ,many distinct ...
0 votes
1 answer
118 views

What could be the reason of having a lower RMSE than MAE?

I used some machine learning algorithms in my dataset and I found that my RMSE goes low than the MAE. What are the most common reasons for that type of typical scenario. Since from my understanding ...
1 vote
0 answers
299 views

TensorFlow 2: Find MAE, RMSE for validation dataset in time-series LSTM

TensorFlow 2 "Time series forecasting tutorial" (https://www.tensorflow.org/tutorials/structured_data/time_series#recurrent_neural_network) gives an example of a LSTM multi-step prediction model that ...
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3 votes
1 answer
397 views

Confusion about the MSE ERROR

I created a Random Forest regressor model and calculated my own error. I also want to calculate MAE, MSE and RMSE to compare my results to similar use cases. I am confused by the results as the values ...
3 votes
2 answers
2k views

Reason for generally using RMSE instead of MSE in Linear Regression

In linear regression, why we generally use RMSE instead of MSE? The rationale I know is that it's easy to minimize the error in RMSE instead of MSE by Gradient Descent, but I need to know the exact ...
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2 votes
2 answers
3k views

What is the difference between an RMSE and RMSLE (logarithmic error)? [closed]

RMSE vs RMSLE Root Mean Squared Error (RMSE) and Root Mean Squared Logarithmic Error (RMSLE) both are the techniques to find out the difference between the values predicted by the machine learning ...
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