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Multiple XGBoost models or just 1 for a cetain type of category?
Python: How to make model predict in a generalized manner using ML AlgorithmWhat approaches could be used to teach a model to make appropriate brush strokes to reproduce a painting?Machine learning algorithm which gives multiple outputs from single inputWhen creating a classification model, should predictors with little correlation to the response variable be included in the model?Which loss function to use for predicting traffic vehicle count?How do I combine two electromagnetic readings to predict the position of a sensor?How to deal with overestimation of small values and underestimation of high values in XGBoost?Regression using gradiant boosting - smoother predictionsforecast product demand in one week using machine learning approachMean Absolute Error increasing with more correlated factors
$begingroup$
I am building a model to predict, say house prices. Within my data I have sales and rentals. The Y variable is the price of either the sales or rentals. I also have a number of X variables to predict Y, such as number of bedrooms, bathrooms, meters squared etc.
I believe that the model will firstly make a split on the variable "sales" vs "rentals" as this would reduce the loss function - RMSE - the most.
Do you think it is best to train 2 models one for "sales" and the other for "rentals"? The RMSE for the model is quite high and this is in part due to the incorrect "Sales" predictions.
machine-learning xgboost
asked yesterday
user113156user113156
1437
$endgroup$
add a comment |
$begingroup$
I am building a model to predict, say house prices. Within my data I have sales and rentals. The Y variable is the price of either the sales or rentals. I also have a number of X variables to predict Y, such as number of bedrooms, bathrooms, meters squared etc.
I believe that the model will firstly make a split on the variable "sales" vs "rentals" as this would reduce the loss function - RMSE - the most.
Do you think it is best to train 2 models one for "sales" and the other for "rentals"? The RMSE for the model is quite high and this is in part due to the incorrect "Sales" predictions.
machine-learning xgboost
asked yesterday
user113156user113156
1437
$endgroup$
add a comment |
$begingroup$
I am building a model to predict, say house prices. Within my data I have sales and rentals. The Y variable is the price of either the sales or rentals. I also have a number of X variables to predict Y, such as number of bedrooms, bathrooms, meters squared etc.
I believe that the model will firstly make a split on the variable "sales" vs "rentals" as this would reduce the loss function - RMSE - the most.
Do you think it is best to train 2 models one for "sales" and the other for "rentals"? The RMSE for the model is quite high and this is in part due to the incorrect "Sales" predictions.
machine-learning xgboost
asked yesterday
user113156user113156
1437
$endgroup$
I am building a model to predict, say house prices. Within my data I have sales and rentals. The Y variable is the price of either the sales or rentals. I also have a number of X variables to predict Y, such as number of bedrooms, bathrooms, meters squared etc.
I believe that the model will firstly make a split on the variable "sales" vs "rentals" as this would reduce the loss function - RMSE - the most.
Do you think it is best to train 2 models one for "sales" and the other for "rentals"? The RMSE for the model is quite high and this is in part due to the incorrect "Sales" predictions.
machine-learning xgboost
machine-learning xgboost
asked yesterday
user113156user113156
1437
asked yesterday
user113156user113156
1437
asked yesterday
user113156user113156
1437
asked yesterday
user113156user113156
1437
asked yesterday
user113156user113156
1437
1437
add a comment |
add a comment |
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