Data Science Stack Exchange
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Questions tagged [svr]

19 questions
3
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1 answer

SVR is giving same prediction for all features

I'm creating a basic application to predict the 'Closing' value of a stock for day n+1, given features of stock n using Python and Scikit-learn A sample row in my dataframe looks like this (2000 rows) Open Close High Low …
Ben Williams
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1 answer

Should the output of regression models, like SVR, be normalized?

I have a regression problem which I solved using SVR. Accidentally, I normalized my output along with the inputs by removing the mean and dividing by standard deviation from each feature. Surprisingly, the Rsquare score increased by 10%. How can one…
ChiPlusPlus
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1 answer

How we can have RF-QLearning or SVR-QLearning (Combine these algorithm with a Q-Learning )

How we can have RF-QLearning or SVR-QLearning (Combine these algorithm with a Q-Learning )? I want to replace the DNN section of Qlearning with a RF or SVR but the problem is that there is no clear training data that I can put in my code at…
user10296606
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1 answer

Why do I get an ValueError for an SVR model with RFE, but only when using pipeline?

I am running five different regression models to find the best predicting model for one variable. I am using a Leave-One-Out approach and using RFE to find the best predicting features. Four of the five models are running fine, but I am running into…
Clarius333
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1 answer

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 weights were calculated using cross…
MyName
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1 answer

NotFittedError says this StandardScaler instance is not fitted yet while using inverse_transform()

I have a dataset and i have used Support Vector Regression.So i needed to use StandardScaler module from sklearn.preprocessing fro Feature Scaling. After training my model when i came to predict it was giving a prediction which was Feature…
Mehedi Hasan Shifat
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2 answers

Scalling and unscalling of data for SVR prediction

I'm trying to use SVR to predict a certain feature. I create the model with the following code: from sklearn.svm import SVR from sklearn.preprocessing import StandardScaler X = data # this is the outcome variable y = data.iloc[:, 10].values sc_X =…
MyName
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Combine AdaBoost and Support Vector Regression?

I have read several papers about using SVM instead of decision tree in AdaBoost, but I haven't seen any papers about using support vector regression (SVR) in AdaBoost. However, if using support vector regression in AdaBoost, I have to weaken SVR. So…
toantruong
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Understanding Support Verctor Regression (SVR)

This question also asked on another StackExchange with Bounty. Question here. I'm working with SVR, and using this resource. Erverything is super clear, with epsilon intensive loss function (from figure). Prediction comes with tube, to cover most…
GensaGames
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spatiotemporal prediction using support vector regression

i am using a geographic dataset and i intend to use SVR as machine learning method for predicting spatiotemporal patterns from this dataset. My question is does SVR canensure spatiotemporal prediction from geographic datasets?
Azza Ousji
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Is my SVR model doing the right thing?

I am doing wind forecasting modeling using SVR model and I wanted to compare the actual wind speed data and the predicted data. I am not sure what I did wrong. I have pre-process the datasets and normalize it. > > # Define column names …
Kuromi
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1 answer

Unexpected behaviour of Scikit-Learn SVR

I'm using Scikit-learn to fit a support vector regression on a really simple dataset of car stopping distances vs car speed. My code for applying SVR to this dataset is: # Import libraries from sklearn.svm import SVR import numpy as np import…
oweydd
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Strong bias from Linear SVR meta model

I have built nine meta models based on the model stacking principle, which I compare to a reference model for a number of time series. See the results below. The 22 base models that are trained on 70% of the training data produce forecasts on the…
Tim Stack
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Why would a Linear SVR model greatly outperform a Linear Regression model on model stacking

I have built nine meta models based on the model stacking principle, which I compare to a reference model for a number of time series. See the results below. The 22 base models that are trained on 70% of the training data produce forecasts on the…
Tim Stack
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1 answer

Get negative predicted value in Support Vector Regresion (SVR)

I am doing Covid-19 cases prediction using SVR, and getting negative values, while there should be no number of Covid-9 cases negative. Feature input that I was used is mobility factor (where have negative data) and daily cases of Covid-19. Kernel…
Ardy
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