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Questions tagged [regression]

Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.

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Evaluating regression model in python - How do I interpret my results?

I'm trying to predict some 'malicious score' using RandomForestRegressor, and i'm trying to evaluate my model performence. I've tried all of scikit-learn documentation functions to evaluate, but not ...
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Parametric set of function regression models

Can someone explain me how we define the parametric set of functions because I have been confused. For example if we have the parametric set of linear functions $f_\theta: \mathbb R^2 \rightarrow \...
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using regression model to optimize teams working on work items

I have a few work items with these features: WI1, WI2, WI3 which describe these work items. I also know the number of people and how many minutes they spend each ...
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How to create autoregression model for multiple inputs and one output with time interval 120min using python

Here I have data with time import from csv file. I have three inputs (x,x1,x2) with actual output value (y) with time period. I want to predict the next value using past values with time using ARIMA ...
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problem in shaping unbalance panel data to run mixed multinomial regression?

I have followed 'mlogit' paper to prepare my unbalance panel data for multinomial (panel) regresssion, but I failed to run the panel regression. After reading some posts, I beleive I have a data ...
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How to identify/Analyze HOG feature data is linearly separable or not?

Work Objective: I am working on Object tracking, I have extracted the Feature vector for ROI(Region of Interest)/template from Image, using this features i am looking to identify most probable ...
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1answer
18 views

Limits of using a normal distribution in Bayesian inference

When applying a Bayesian inference method such as Gaussian Process Regression (GPR), the assumption of a prior and likelihood function following a normal distribution is inherent. One can use an ...
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With a continuous target variable and a pre-defined cutoff, should I do classification or regression?

In my machine learning project, the target variable is continuous. Also, we have a pre-defined cutoff, which can separate the target variable into two classes (e.g. High and low). There are two ...
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Comparing the performance (reward) of dqn and logistic q-learning?

I have tried to compare my DQN results (rewards) with logistci q-learning (omitting the hidden layer, just inputs and outputs with a sigmoid activation function) My rewards of logistic-Q-N is about 5-...
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normalize output of a feed-forward ANN

My feed-forward neural-network is modeling (regression) a multi-channel loss function. The output of the network is a vector y (size 10) that describes the loss ratio of the input signal x for each ...
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1answer
55 views

Right Regression Model to use

I am trying to predict reservation count from a dataset with few features. Features are both categorical and continuous. The dependent variable reservations looks like below: My dataset size is ...
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Does integrating data points that fall under a curve make sense?

I have data I've collected from the fanfiction website, Archive of Our Own. I want to know if there's a relationship between the length of a chapter and the average number of hits per chapter. I ...
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How to make multiple regression perform better for outliers? (without reducing effect of them)

I have a small dataset(about 60 samples) and I need it to predict well for high target values. There are only a few high values and all models I tried perform poorly for these high values. So I ...
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Is it possible to group/tag sets of input features for learning?

I've been using various multi-output regression algorithms in scikit-learn successfully prior to this. Supervised regression with input-output mappings, and ...
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2answers
66 views

How can I check the correlation between features and target variable?

I am trying to build a Regression model and I am looking for a way to check whether there's any correlation between features and target variables? This is my ...
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1answer
25 views

Should I transform a multiple regression with outliers into ordinal regression?

I have small dataset of about 60 samples that performs poorly in regression. So I wonder how can I transform this task into predicting intervals instead of values. Is it possible to make it perform ...
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2answers
44 views

Neural network for Multiple integer output

I have a data set that contains 135 input features and 132 output values to be predicted. The input features are all numeric floating point values and each output value would be an integer between [0,...
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1answer
15 views

Loss function when the output is a single probability

I have a regression problem where the output y is a single probability, i.e. real number that varies in the interval [0, 1] ...
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23 views

Parameter extraction from a spectrum

Every plant leaf has a spectrum from 400-1000nm possibly depending by parameters like: temperature, moisture, sunlight, soil. I have a dataset of leaves with respective parameter values and a ...
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25 views

Test and compare Model Performance

I have a 93 x 41 data frame in r, with three response variables, one numerical, one binomial and one multinomial. I'm employing various feature reduction methods (VIF, PCA, PCA+ICA, Relief) and ...
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Comparing XGBR with CatBoost performance

I saw on a CatBoost site that it supposed to outperform any other boosted training model and decided to try it myself on a Kaggle's https://www.kaggle.com/c/house-prices-advanced-regression-techniques....
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1answer
11 views

Dealing with diverse groups in regression

What happens if a certain dataset contains different "groups" that follow different linear models? For example, let's imagine that examining the scatterplot of a certain feature $x_i$ against $y$ we ...
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1answer
10 views

Forward or Backward Stepwise Regression

I understand the process and logic of why to perform stepwise regression. To me they should always arrive at the same function, just one adds coefficients and tests for significance while the other ...
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Genarate one hour time interval array using pandas in python (import from csv) to predict next value

I am trying to generate one hour one hour time interval to predict next value according to my data set imported from csv file. Here according to the time it will give outputs include in x column. This ...
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23 views

Do I choose regression tree split point randomly?

I am interested in using random forest for regression purpose and I understand that random forest is an ensemble of regression trees. What makes random forest different is instead of finding the best ...
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1answer
16 views

Does bagging create iid trees?

As the title suggests, I have a question regarding the trees produced through the bagging procedure. Namely, since the bootstrap samples created to fit trees on are independent and identically ...
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1answer
18 views

Why is the logistic regression decision boundary linear in X?

The logistic regression model, \begin{equation} \operatorname{p}(X) = \frac{\operatorname{e}^{\beta_0 + \beta_1 X}}{1 + \operatorname{e}^{\beta_0 + \beta_1 X}} \end{equation} is said to create a ...
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Build algorithm for Price Prediction/Classification [closed]

lets say I have historical data for prices and some additional information like article, location and maybe text like "higher price in this area due to less competition". Important is, that prices are ...
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2answers
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Regression or Correlation for this RQ?

Our little group at uni is investigating if there is a relationship between 3 measures of social well-being (social anxiety, social connectedness and self esteem) and usage time (on-screen time in ...
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1answer
33 views

unable to plot contour/surf graph of cost vs parameters for multiple linear regression.In python

After running gradient descent I have three arrays theta1 theta2 and J all three of size num_iterationx1. I tried plotting contours using the contour function in Axes3D and 3d surface plot using surf ...
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Classification/Regression Problem where Response Variable is Ordinal

Data Science novice here! I'm trying to work on the white/red wine quality data set, where I 'm trying to predict the quality of the wine. All the features are numerical. The response variable ...
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Regression of discrete continuous averaged data

If I have data that is continuous and discrete that is the average of a timestep. For example average power usage over the past 1 hour that is continually sampled every hour. How could I turn such ...
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1answer
42 views

Timestamps in Ridge Regression Scikit Learn

I am trying to transform data for use in regression, most likely the Ridge or Lasso technique implemented in sklearn.linear_model. My training data contains time ...
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1answer
56 views

Predict future value with time period using non linear regression model [closed]

Here I have dataset import from csv file. I want to predict the next value with the time series. Can we use nonlinear regression model to predict the value for next time period or Is there any ...
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1answer
18 views

multi-targets predict using python

let's say I have year month data h1 h2 h3 .. h24 2004 1 1 40 42 60 .. : : : 2008 12 31 I am trying to predict h1....h24 for a ...
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1answer
32 views

Which of the following linear regression model is better?

Consider 2 regression models on the same data set. Model 1 : $R^2 = 90$% , $R^2(adjusted) = 80 $%, $R^2(pred) = 70$% Model 2 : $R^2 = 60$% , $R^2(adjusted) = 59 $%, $R^2(pred) = 58$% In the first ...
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1answer
14 views

Can I treat text review analysis as a regression problem?

I am playing with a dataset that contains tripadvisor restaurant reviews and their labels (either 1, 2, 3, 4 or 5 stars). Initially I was thinking of using it as a classification problem, applying ...
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2answers
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Is there a definitive and more conclusive way of interpreting the R^2 score from a linear regression model in terms of prediction accuracy?

I'm trying to find a definitive way to conclude the R^2 score from a prediction accuracy point of view rather than variance. How should I do it? Conceptually, most blogs / articles explain R^2 as: ...
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Predicting both - classification and regression with time series random forest

so I'm developing a sports book betting system. So my goal is to choose appropriate ML approach to predict client's next bet based on the history of other clients' bets. Its regression and ...
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1answer
17 views

How to check if the dataset contains sufficient information to predict a continuous variable

Is there a way to check if a dataset generally contains enough information to predict a target variable? In other words, can you calculate something like a correlation-coefficient between all ...
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1answer
27 views

bad regression performance on imbalanced dataset

My current dataset has a shape of 5300 rows by 160 columns with a numeric target variable range=[641, 3001]. That’s no big dataset, but should in general be enough for decent regression quality. The ...
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How do I apply Kalman filter to machine learning model?

Suppose that I have an input as a rotation of my hand (Quaternion: x, y, z, w) and I want to predict the position of my hand relative to my shoulder (x,y,z). The inputs are the rotation of each joint ...
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1answer
30 views

Regression model performance with noisy dependent variable

I'm doing a support vector regression with the dependent variable representing measurements from an uncalibrated sensor (measurement error between 2% and 20%) and I want to study the effect of this ...
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1answer
33 views

Decomposing R squared or VIF

In the context of multi-regression, I am wondering if there is a way to decompose $$VIF_i = 1/(1-R_i^2)$$ where $R_i^2$ is the r squared obtained from the regression of dependent variable = i and ...
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12 views

Does stochaistic gradient descent perform better and Adam optimizer on small data sets?

I have noticed while comparing different optimization algorithms that SGD performs better than Adam optimizer when data set is smaller than a particular limit. What might be the mathematical reason ...
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0answers
44 views

demand forecast for B2B

I am attempting to create a demand forecasting model in python to predict future sales of a particular category of product, using historical sales data. We are a B2B company, which means that we ...
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2answers
17 views

How to perform a reggression on 3 functions using a Neural Network

I am currently building a neural network using Keras to perform a regression. I have 4 independent variables W,X,Y,Z. They are used to predict 3 different ...
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17 views

Time series with multiple attributes and multiple groups

I am working on this dataset UK Traffic Dataset. Here is my sample kernel : my kernel on UK traffic data This dataset consists of several groups and it has date and hour , as it is hourly time ...
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Test set accuracy higher than training set accuracy for Ridge regression

I'm conducting a Ridge regression between two different data but for the same entity, so my equation is like $Y = mX + c$. I'm using the train_test_split function ...