Questions tagged [predictive-modeling]

Statistical techniques used for predicting outcomes.

Filter by
Sorted by
Tagged with
0 votes
0 answers
21 views

Why kind of data is appropriate for prediction and what kind is not? [closed]

What relationship between data is appropriate for predictive modeling and what type is not? How do we ensure we select the appropriate type of data for predictive modeling and not use data that is not ...
HelloDarkWorld's user avatar
0 votes
1 answer
18 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
  • 137
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
23 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
16 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
47 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
17 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
8 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
32 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
44 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
51 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
27 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
15 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
62 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
41 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
27 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
23 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
14 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
31 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
12 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
33 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
274 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: ...
Display Name is missing's user avatar
0 votes
0 answers
20 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
34 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
148 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
  • 414
0 votes
0 answers
67 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
17 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
9 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
76 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
0 votes
0 answers
19 views

Seeking guidance on data analysis techniques to estimate project LOE based on numeric counts of outputs produced

I’m looking for some basic guidance on where to focus my research in support of some data analysis I’m looking to perform. The problem space is identifying a methodology for estimating Level of Effort ...
Steven's user avatar
  • 109
0 votes
0 answers
15 views

Associating two variables in XGBoost model

I am trying to build a model that will predict a college football players probability of being drafted. I have multiple variables with different athletic measurements, but for the sake of example lets ...
user153943's user avatar
0 votes
1 answer
90 views

How to compare test vs train model performance

When comparing the test vs train model performance to ensure no overfitting (e.g., using AUC ROC as an example), is it better to select the model with the largest test score, or the model with the ...
thereandhere1's user avatar
2 votes
3 answers
373 views

What does it mean order of input sequence does not matter for transformer self-attention head?

The need for positional encoding in transformer models is justified by permutation invariance of self-attention heads, because, without it, transformer wouldn't have any mechanism to take into account ...
DeLorean88's user avatar
0 votes
1 answer
14 views

Modelling employee workload data

Wondering if I can get steer on this question. I have a dataset with the columns -date,employee id, task id, volume of work completed as percentage (float) for that task, time taken to complete that ...
Santosh's user avatar
0 votes
0 answers
40 views

Survival analysis on time series data for predictive maintenance

I want to train a survival analysis model for predictive maintenance on an asset (confidential, let's say it's a motor). The dataset consists of hourly readings of multiple sensors, the type of motor, ...
rvdinter's user avatar
1 vote
0 answers
38 views

ML with Python - extract information from user text

I'm a web developer, got a little experience with Python but none on ML. Tour operator customer want to introduce AI/ML on his website, the goal is to have a single text input where user can prompt ...
fudo's user avatar
  • 111
-1 votes
1 answer
24 views

Prediction with 2years data

I have this data https://data.mendeley.com/datasets/3g8dtwbjjy/1 and I'm asked to make a prediction task but I don't quite understand how I should do that. I need to use 3different algorithms for it ...
Alia Oumar's user avatar
0 votes
0 answers
26 views

Fine-tuning Pretrained Models for Web DOM Interaction Prediction Task

I am currently working on a side project that involves predicting changes to a webpage's DOM based on user interactions. The idea is to input the initial DOM state and a user interaction, and predict ...
DonnyDato's user avatar
1 vote
1 answer
63 views

improving Neural network regression model

I have the following toy data (which closely mimicks my original larger data used for the project): ...
Ayan Mitra's user avatar
0 votes
1 answer
36 views

Time series prediction problem with other parameters

My data looks like this: time series of average house prices per day in 100 cities (maybe more) over a two-year period, each with a set of characteristic variables that do not change over time, such ...
Daye Tao's user avatar
0 votes
0 answers
18 views

Help creating a SCORING model/formula.......am I on the right track?

Trying to create a basic machine scoring model, that takes in 4 parameters: Number of maintenance events Years of life left Manufacturer support (bit - either yes or no) Visual condition The ...
manavjn's user avatar
1 vote
2 answers
35 views

Simplex method for equality optimization

I have a linear model that goes as 0.1*x1 + 0.8*x2 + 3.4*x3 + 5.0*x4 + c and this linear model was generated by using a Linear Regression. MAE is ~ 0.4 MSE is ~ 0....
anthino12's user avatar
  • 111

1
2 3 4 5
24