Skip to main content

Questions tagged [predictive-modeling]

Statistical techniques used for predicting outcomes.

Filter by
Sorted by
Tagged with
0 votes
1 answer
32 views

Build a Neural Network for Multi-output Regression

I have a network model that accepts about 25 inputs and outputs 3 actions. The outputs are: delta X and delta Y of the robot and the angle of the robot. After I enter the data into the model, I get ...
May's user avatar
  • 1
0 votes
0 answers
8 views

Combing Output of Two Regression Models

I have two models. Model 1: I have a dataset of American high school students and their test scores and other characteristics. I built an ARDRegression model that predicts how well a student will ...
Mary's user avatar
  • 1
0 votes
1 answer
24 views

handling predictions with optional or missing features

We have a few variables that are highly predictive in our modeling task. Is it sound to train models with a superset of features even though some are known NOT to be available at predict time? & ...
eliangius's user avatar
  • 351
0 votes
0 answers
12 views

Machine Learning Algorithm for identifying the factors contributing to academic performance of students

I have a dataset with several qualitative and quantitative attributes, including age, location (longitude, latitude), city, parent occupation, family size, GPA etc. My task is to find the attributes/...
Dawood Ahmad's user avatar
0 votes
0 answers
23 views

How to predict inside of the box temperature at a give outside temperature using python?

I Need help predicting inside of the box temperature at a given outside temperature. Background I have a system (also known as a BOX). The BOX is insulated from the outside environment and contains ...
Dennis's user avatar
  • 1
0 votes
0 answers
8 views

Best subset selection includes predictor with high p-value

I'm trying to use subset selection to find out the best model according to AIC. However, it is recommending a variable with a p-value > .9. My guess is that this is because the subset selection ...
cardinalcat27's user avatar
0 votes
0 answers
9 views

İnternal and hold out test for shap

There is an internal test set and a hold out test set. I explain the model with Shap library. Should I use the internal test set or the external test set? What should I do if there is a difference?
Nemo's user avatar
  • 1
0 votes
0 answers
4 views

How do I determine syndicate and collusion indication with clustering and network analysis on a large unlabeled user transaction data?

How do I determine syndicate and collusion indication with clustering and network analysis on a large unlabeled user transaction data? So far, I've only been training with labeled data on fraud-...
user161454's user avatar
0 votes
0 answers
15 views

Select estimators for Voting classifier

I don't know the efficient method to select the best subset of the estimator that saves time and highest accuracy. How to choose the best estimators for the Voting classifier? Voting Classifier is an ...
Davann Tet's user avatar
0 votes
0 answers
12 views

ANFIS Model, tips about improving performance

I have a question regarding about improving the performance of an ANFIS (adaptive neuro Fuzzy inference system) model. In MATLAB, I have been training a model with 5 inputs, with 816 data point for ...
jocelyn matus ancavil's user avatar
0 votes
0 answers
10 views

how to obtain predictions for all observations in a dataset?

I have a dataset comprising 1864 roads with various traffic and safety features, along with the number of accidents at each road. The objective is to rank these roads based on the number of accidents. ...
ebrahimi's user avatar
  • 1,307
0 votes
0 answers
10 views

LSTM Model for Multivariate Multi-Series

I'm looking to create an LSTM model to predict a certain label trained on multiple short-time series data. How would I go about doing this? Each time series has 10-30 time steps and 20 different ...
Chino's user avatar
  • 1
3 votes
1 answer
64 views

When do you know training a model is not feasible?

A couple weeks ago I volunteered to take on a project at work to try predicting the ideal price rental cars my employer should be charging based on our historical rental data. The variables available ...
enmasse's user avatar
  • 31
0 votes
1 answer
20 views

Does ANN returns the same prediction for the same input?

Does ANN predictable? By this I mean that if I re-run the same script over and over, does it make sense that the error (MAE / MSE / R^2) is different on every run? if true, then a follow-up question: ...
Cohensius's user avatar
  • 163
1 vote
0 answers
33 views

Standard Error interpretation help

I performed a standard error on my machine learning model to predict protein structure. The graph Im showing here is a snippet of the actual data and I deleted some irrelevant info. The y axis is the ...
hypermiler3's user avatar
0 votes
0 answers
19 views

Differentiating between runs that start at different timestamps

I have a CSV file of the latitude and longitude coordinates of trains in the format [timestamp, runID, latitude, longitude]. The problem is that I have tracked ...
make nice rhombus's user avatar
0 votes
1 answer
29 views

What's the best model choice for a non-linear regression task?

I have a dataset with the following format: Rows: 3700, float_columns: 17, int_columns: 2, categorical_columns: 12 Target Type: Continous, float My dataset is an insurance dataset that stores the ...
Connor's user avatar
  • 631
1 vote
0 answers
21 views

How to Yield a Better AUC / Lift Score?

I have a dataset with 200k records and 173 features focused on binary classification. Class proportion is around 98.7:1.3 (1.3% target=1). Currently, I am trying to increase the performance of my ...
DM_FCP's user avatar
  • 11
0 votes
1 answer
52 views

Sale Forecasting Problem -- Is it legit to use inventory level as a feature?

I'm working on a project to predict future sales for our company's products so that the supply chain can have better idea how much to restock. Detail about the model I'm working on: Model: LGBM (from ...
user159479's user avatar
0 votes
0 answers
18 views

How to use two independent datasets in machine learning phd research work?

In order to develop an academic performance prediction model for a local Higher Ed Institution, I have collected the OULAD open dataset and the local Institution's dataset which I structured into the ...
AnilPHD's user avatar
0 votes
0 answers
12 views

Prediction with optional parameters

I want to build a machine learning model that has 25 input feature and two labels (my problem is a regression task if it makes any difference), to my knowledge since I have two labels I need two ...
Aws rayyan's user avatar
0 votes
1 answer
41 views

Ideas on building models to predict the likelihood of prospects converting (making their first purchase)

context: I have a task to identify the prospects who have high or medium likelihood of making their first purchase after they signed up for 30 days, so that our marketing teams can take actions for ...
Iris's user avatar
  • 3
2 votes
2 answers
53 views

Handling Month-over-Month data in Regression Model

I have data similar to what you see in the picture. I want to use a RandomForest Regression model where I can use fields (excluding MONTH_END_DT and LOCATION_ID) to predict REVENUE_PER_UNIT. The idea/...
Larry Burholme's user avatar
0 votes
0 answers
26 views

Using simple RNN to identify a simple dynamic linear system

I have been trying to identify a simple linear second order system (e.g. a pendulum or a mass-spring system), by simulating it in Python using backwards-euler method and then feeding the step changes ...
APasagic's user avatar
0 votes
1 answer
60 views

Effect on regression coefficients by multiplying a constant to a feature

I was solving one quiz question on Coursera and I found an interesting question. If you double the value of a given feature (i.e. a specific column of the feature matrix), what happens to the least-...
teddcp's user avatar
  • 165
0 votes
0 answers
16 views

How to approach this problem?

I have 3 large datasets with 21 non-ordinal categorical variables and 1 variable date, representing data from month intervals in two years. The target variable is a categorical variable with multiple ...
memoryless_statistician's user avatar
0 votes
1 answer
30 views

I am a beginner in Knime and using Random Forest

Within Knime how am I able to identify which of the variables have the best predictive power within my model. I have successful ran the model and assessed model accuracy, recall and precision but can ...
skillzuko345's user avatar
0 votes
0 answers
27 views

How to reduce number of continuous variables before I make a set of best predictors (for handgrip strength in women)

My assignment question is quoted: "2. Which set of variables best predicts handgrip strength in women? a. Reduce the number of continuous variables before doing the analysis." I do not ...
Nathan Vermaerke's user avatar
0 votes
0 answers
22 views

LightGBM Regressor miscalibratred/underestimating on high fitted values and overestimating on low fitted values

I'm training a pretty standard LightGBM regressor and noticing a strange pattern with the residuals (see images below--I'm bunching the predicted values and taking the observed average for the group). ...
dfried's user avatar
  • 101
0 votes
2 answers
68 views

Best model for regression in this case?

I am doing some modeling to predict a variable of interest given a big set of features (500) for which I expect a considerable amount of interactions happening at least among some of them. I first ...
Mirko's user avatar
  • 111
1 vote
1 answer
43 views

Is there an appropriate hypothesis test: Two samples, one only with one datapoint, non-normal?

I have two 'samples'. The first consists of approx. 400 physical measurements of a quantity (taken over one hour, and the real situation is not a steady state). They show a very skewed distribution ...
Mars's user avatar
  • 11
1 vote
1 answer
29 views

How to impute and aggregate data with ID variant variables for predictive modeling?

I have a dataset that looks like so ID var_id_invariant_1 ... var_id_invariant_p var_id_variant_1 ... var_id_variant_k target 315 25 ... a 2.4 ... A 1 246 31 ... nan 5.7 ... B 0 315 25 ... a 9.4 .....
Mateusz's user avatar
  • 125
0 votes
1 answer
24 views

Is there a standard data science workflow/decision tree?

I'm looking for some kind of reference that essentially shows an example of an entire data analysis workflow beginning with feature engineering, and ending with analyzing the results. I know the ...
David's user avatar
  • 101
0 votes
0 answers
18 views

Combining Computer Vision and traditional machine learning to predict respondent reactions (from a survey) to seeing a picture. Is it possible?

I have the following task Computer Vision/prediction task which I’m interested in hearing whether you guys think is feasible. I have a dataset of 1000 respondents coming from a survey, where ...
Andy's user avatar
  • 11
0 votes
1 answer
36 views

LSTM For Predicting Vector Sequences

I am attempting to construct a Keras model that intakes a sequence of vectors and outputs the most likely next vector in the sequence. I have followed a few tutorials, but nothing is quite seeming to ...
slastine's user avatar
0 votes
0 answers
14 views

Why is my Histogram Gradient Boosting Classifier model still producing type II error? How can I reduce the type II error?

Type 2 error and how to hypertune or feature engineer a solution for it I trial and tested different techniques and kept the structure which made the most sense to me. But still my model confusion ...
bitebytebit's user avatar
0 votes
1 answer
35 views

For some reason getting an odd error when hyper tuning my model

I was hoping to hypertune my decisiontree model , however I keep running into this error: TypeError: DecisionTreeClassifier() got an unexpected keyword argument 'criterion' here what I tried: ...
John Adams's user avatar
0 votes
1 answer
550 views

ValueError: X has 54 features, but DecisionTreeClassifier is expecting 53 features as input

I am analysing and prediction 2023 Cricket World Cup based on previous given dataset. This is Exploratary analysis: Feature selection and Training model: Applying Random forest classifier algorithm: ...
Mithlesh Upadhyay's user avatar
0 votes
0 answers
22 views

overfitting or not

Hello so i'm building a classification model i train my on various models and these are the metrices so i want to know if ther's an overfitting or not
Bilel kort's user avatar
0 votes
0 answers
18 views

Minimize MAE loss for a target that is sum of two other targets

Working on a regression modelling task where my dataset have some feature columns, two more columns A, B and a target column T. The goal is to predict T, and minimize MAE, that is ...
Ngo Cuong's user avatar
0 votes
0 answers
22 views

Model Performance not improving

I am currently working with a GNN (a Graph attention Model) based model and the main task is to do Graph prediction. My model doesnot improve its performance when I change the number of heads or the ...
Susan's user avatar
  • 1
0 votes
0 answers
8 views

Predicting a finite multi-set from a finite set given vector-valued data

I am unsure in how to approach this problem, which is a supervised learning task, and was looking for ideas on how to tackle it since I am kinda confused. Abstract description: I have a source set $S:=...
The Bosco's user avatar
  • 101
1 vote
1 answer
42 views

Feature selection for propensity model

I'm trying to build a propensity model for whether or not a customer will buy a second product. I was given data that looks like this: | Age | Income | DaysSince1stPurchase | Bought2ndProduct | |:---- ...
BlueSkyz's user avatar
0 votes
2 answers
155 views

What is the state-of-the-art in prediction\classification missing labels in partially labeled data?

Overview Let's say I have the following data: ...
Mario's user avatar
  • 400
0 votes
0 answers
68 views

Prediction in multiclass classification

Context: I need to make an multiclass classification to predict what type of sentence(law) the case will have in the end. Data: I Have several columns to predict the case:client, cause of action, ...
TM01's user avatar
  • 1
0 votes
0 answers
7 views

Dynamic dosing recipe for accurate pH

I want to have a script for adjusting dosage of ingredients in each batch dynamically. Assuming that the requirement is to have a specific value of pH from each batch but with the variation of raw ...
Pittoon Hanvivatpong's user avatar
0 votes
0 answers
22 views

How to solve this error in R Error in mls(Log_change, 1:11, 52/12) : Incomplete high frequency data?

...
J_Bake's user avatar
  • 1
0 votes
0 answers
25 views

Growth (sales) forecast based on season

How to make a forecast based on last year (season)? I have no experience in the field Make a forecast of the sales in september 2023 based on last years sales Example A Sales august 2022 (last year): ...
clarkk's user avatar
  • 101
0 votes
0 answers
11 views

Create Dataset (ADS) to build predictve model for servey data

I have collected labeled survey data spanning 8 months in 2023, capturing satisfaction levels ranging from "very satisfied" to "very dissatisfied." My objective is to develop a ...
Mohamed SELAMA's user avatar
1 vote
1 answer
83 views

Dealing with varying predictive horizon

I know that the predictive horizon is the time window that runs from the observation of the data to the manifestation of the target variable. But how can I deal with prediction if the time horizon ...
Marco Ballerini's user avatar

1
2 3 4 5
24