Questions tagged [predictive-modeling]

Statistical techniques used for predicting outcomes.

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17 views

How to present longitudinal data to LSTM for multiclass prediction

I need to implement a deep learning algorithm to predict an ordinal value, called 'Entity', using longitudinal health records data. I read a few articles and guides but I couldn't find a clear ...
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1answer
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How to interpret classification output - Predective model

What is the significance of macro avg ? I'm not sure if this report signify a good predictions by the model. Thanks in advance.
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1answer
15 views

Classification Based Collaborative Filtering Model

I was going through algorithms for collaborative filtering-based prediction. Most of the places, I read about using matrix factorization based on ratings of the likeness of the user. But for my use ...
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1answer
24 views

Prediction Algorithm for Data with high Randomness

I have data for the orders of the previous year containing the product and the seller who sold the product. I have an information product, product category, seller, delivery address price etc. ...
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Do Any Frameworks Provide Better Support for End-To-End Integer-Based Feature Engineering, Modeling, and Inference?

A retail enterprise I work with with wants to switch from its home-grown time series data analysis and prediction system to something more established and with community support. One unique feature ...
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How to handle features containing strings in XGBoost in AWS Sagemaker

How can i handle the string containing spaces and colons as a feature for my xgboost classifier model? AWS Sagemaker requires the input in csv format, I don't know how to convert the string to the ...
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Oversampling Using the Orange Data Sampler Widget

It was pointed out in the help section of the Data Sampler widget that it could be used for under or oversampling. I used the Attrition dataset where the class imbalance is 1233/237. I separated the ...
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1answer
28 views

How to model the probability of detecting an image, given it is seen multiple times

Are there any existing methods/models describing the probability of an object being detected by a computer vision algorithm given it is seen $n$ times at similar angles and orientations? I know that ...
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Which possible models can I use to predict results from this dataset? [closed]

I'm new at predicting things like this. I have a data set. The head of it is shown here: I have yet another data set for the upcoming basketball games that are taking place soon with the score ...
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Density Plot I don't understand?

I'm reading a sample machine learning auto-generated report at https://app.jadbio.com/share/e18eafb5-ba15-4743-925c-2e3b3fe6bbbb. In the bottom of the page, I see: I don't think I understand the plot....
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Prediction with very less data

Here is a question I am struggling with Training data: ...
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Why the prediction of this Random Forrest model is so poor in this machining dataset?

I am using Random Forrest to predict the MRR (Material removal rate). But the predictions have been quite off the mark. Even Linear Regression gave a much better result. I don't know where I'm going ...
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1answer
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Why is shuffling timeseries a bad thing?

I'm trying to understand precisely why it is a bad idea to shuffle time-series when splitting train and test data. Like, what is false about shuffling time-series? How does it tamper with the model?
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1answer
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Mathematical bias and weight vs machine learning bias and weight

I am a little confused about the term Bias and Weight with respect to machine learning. Say we want to predict the heights of people whose weights are given. So plot weights to x-axis and height to ...
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Predicting % of demand going to each product

I work within an industry with products that expire, therefore we would like to be able to choose which specific marketing keywords we should switch on to drive demand to the products that are over-...
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What ML techniques work on imbalanced datasets

I have some specific questions for which I could not find answers in textbooks/research articles. Shall be grateful for an answer. These are: Are there ML techniques that can be directly applied on ...
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Difficulty understanding the difference between Poisson, Quasi-Poisson, and Negative Binomial models

I will try to keep this short. As an assignment for my GLM course, we were given a dataset on the # of homicide victims a person knows, as well as the race of the person. The main idea is to answer ...
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What models should I try with a time series database? [closed]

I've acquired and cleaned a dataset that shows statistics from every county in New York State during 2010-2019 focusing on the NYS School Aid correlating it to other growth and criminal statistics. ...
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Putting a model [sklearn] from Git into production

Currently the DS team provides Python (sklearn) models to put into production. This is considered a devops task. The workaround in lieu of expensive (platform /infrastructure) we went for is using ...
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estimating single or multiple model for Multiple Time Series Forecasting

I am a newbie in the ML field. So please, neglect or better correct, if I am wrong somewhere. I am working on a requirement where details of loading time for each page/component will be given. Now I ...
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25 views

Find top features that determine movie rank

I am trying to analyze a movie dataset in order to find the specific features which determine whether or not the movie is in the top-10 movies of the year (or likewise the worst-10 movies of the year)....
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1answer
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Predict the first observations of a time series when order of the model is higher

Suppose you have you have a time series with 365 observations, one for each day of the year, and you split the first 183 rows in training set and the latest 182 in test set. Suppose you create an AR (...
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Getting low/very uncalibrated predictions on new data for regression using xgbtree method in train() function in R's caret package

I recently tried creating a regression model (imagine a model with the target variable being an integer with values 10-110) using xgboost (method = 'xgbTree') with caret. The model trains successfully,...
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Modeling buying behaviour - where to start?

I'd like to model purchase decisions but I am at a bit of a loss where to start, as the standard ML textbooks don't seem to describe this specific situation which is admittedly a bit odd and complex. ...
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25 views

Predicting when a customer will convert

I want to build a model to predict if a customer will convert from a free product to its premium version. In addition, I'm also interested in knowing when she will convert. This is different from the ...
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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 ...
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History that lead to the word “predict” being used for the application of a model on data

Background The framework scikit-learn uses "predict" for the application of model on (new) input data and I have seen many people use that term. In the scientific papers that I have read (...
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Trying to input a list of strings into my LSTM model

I'm training a model for dialogue act classification. I'm trying to write it so that I can enter a singular list of strings and receive a prediction for each of the strings. I've come to understand ...
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2answers
27 views

How match output (pred value) to input value

I'm working with data(with 4 columns which are p(product), M(name of the store)), I want predict the demand of store for that I sued SVR on the data by theses formulation: ...
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Should I give regularly-spaced or irregular-timestamped data to a price predicting neural network?

I am building an application to predict the price of an item. Data is collected at regular 5-minute intervals while the application is running. Unfortunately, there is downtime, so there is not a full ...
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1answer
23 views

Dealing with unseen data/categories in machine learning models for stream data

I want to build a machine learning model (xgb and lgbm) that has to handle streaming data on a weekly basis. The models are trained on a bi-weekly basis. The data includes order information and I want ...
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1answer
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Does the model(best fitting line/curve) changes when the training data is changed in the cross validation?

From my understanding - a machine learning algorithm goes through the inputs (independent variables) and predicts the output (dependent variable). I believe, what line/curve would best define the ...
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9 views

How to determine activation functions for neural network

I am trying to plan a neural network for regression predictions. The final activation layer should be a linear function, but for hidden layers, do the activation functions need to also be all linear ...
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How to find the relationship between one dataset and two others?

From a STEM problem, I vary a variable x within a range and calculate quantities $U(x)$, $V(x)$ and $W(x)$. I want to figure out an analytical relationship between unknown $U(x)$ and the other two ...
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Is there enough datapoints to make a reasonable predcitction?

I am planning to use this dataset (for a school project) in order to determine the most important features in predicting whether a student will receive a placement. Further I would like to create a ...
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1answer
33 views

Which data science model is best for explainability for prediction problems?

Imagine you have to create a model to explain to stakeholders e.g. to predict price, weight, sales etc.. Which regression models offer the best in terms of explainability and interprability? ... Which ...
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How to best determine trim values for a trimmed mean calculation

I am designing a model for a processing facility. This facility runs certain processing steps on products. Depending on certain details of the product/step, these steps are grouped together into step ...
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How to prepare/optimise data for FP-Growth algorithm

I'm using spark mllib for FP-Growth algorithm for our ML model. Description of my issue: I have taken transactional data from our production database to mine the frequent brought items recommendation. ...
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9 views

Modeling a time series quantity by modeling its constituent time series

I have a time series target, let's say $Y_1$. This quantity depends on two other time-series quantities deterministically, $Y_2 \text{ and } Y_3$. That is, we have some function which takes $Y_2$ and $...
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1answer
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How to find False discovery rate of a ML model? Situation explained below

Lets say:- ML Model denied 1000 transactions FDR= FP/(FP+TP) I randomly picked 400 accounts and checked if the model denied good user or bad user. Good user denied- 110 Bad user denied - 290 How will ...
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Which PreProcessing method should be used?

I have a dataset that consists of a poisson distribution, a exponential distribution, categorical variables, and my target variable is a numerical bimodal variable. This is a regression model. I ...
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51 views

flexibility vs complexity vs number of predictors in machine learning

I'm new to machine learning so am quite confused with the above concepts. It seems to me both flexibility and complexity measures how well the model fit the data (in terms of the curvy-ness), so what'...
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Any tips on transfer learning for a regression problem using 4D images as input?

I developed a CNN based on EfficientNet in order to predict the weight of piles of some materials in an image (the labels are the weights in kg and the input is RGBD tensors of the object). I have two ...
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Impute missing values in feature column on the basis of Target column

I am working on a toy project for insurance claim prediction. In the input data for one of the feature (numeric data type) half of the values are missing. My target variable is binary which indicates ...
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1answer
26 views

Terminology of time series

The terms Time Series Analysis, Time Series Forecasting, and Time Series Modeling are widely ...
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3answers
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Is it possible to change the input columns of a trained ML model while making predictions from it without affecting the accuracy?

Consider the following scenario. I have trained a K-Means model on some input features, say, (A, B, C, D and E). Now at the time of making predictions I want to make the model predict using only fewer ...
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2answers
28 views

Update machine learning model while retaining the prediction results exact for old data

New to ML here. In our industry, we are looking for a type ML method/model that can be updated to accommodate new data points while keeping the prediction value of the historical data exactly the same....
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1answer
21 views

Which data sets would help to predict (exponential) market trends?

Which kind of datasets do websites such as MeetGlimpse, trends.co, explodingtopics.com use to detect exponential market trends? I love them (not affiliated) and would like to better understand how ...
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Forecasting monthly visitor count from daily values

I have a dataset of the daily visitor count of a website. Given this information, I want to forecast what the monthly visitor count will be. Depending on the visitor count on a day of the month, I ...

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