Questions tagged [predictive-modeling]

Statistical techniques used for predicting outcomes.

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

How to best accommodate snapshots of data over time into a single dataset for training

Say we have customers who acquire or not a product, and we have snapshots of the customer's profile monthly, with the information if at that given month they acquired or not (binary label). I have two ...
0
votes
0answers
15 views

RandomForest (RandomForestRegressor) returns weird predictions

I wish to see how different algorithms perform when predicting stocks (using technical indicators as features). When modeling the randomForest (and looking at the graph) I get very bizarre results. ...
0
votes
2answers
17 views

Problem with Logistic regression using categorial variables

I have a problem with Logistic regression using categorial variables. This my training datasets :
4
votes
2answers
2k views

fix first two levels of decision tree?

I am trying to build a regression tree with 70 attributes where the business team wants to fix the first two levels namely country and product type.To achieve this,I have two proposals: 1.Build a ...
0
votes
0answers
27 views

Divergence of Specificity and Sensitivity

I am working on a ML classification project in healthcare. The data is imbalanced, and I decided to start modeling with tree-based algorithms such as (Balanced)RandomForest and XGBoost. While checking ...
1
vote
1answer
15 views

Best forecast model for insurance policies volumes

I am new in forecasting and I am studying a dataset from an insurance company that contains the volume on a monthly basis of new policies, renewals & cancellations. New policies of a given month ...
3
votes
1answer
74 views

Model Guardrails

Suppose I am building a machine learning model for an application where I do not need to make a prediction on all new samples, and given a new sample, it is better to make no prediction at all when ...
2
votes
2answers
25 views

Good approach to increase accuracy for a continuous value that is highly variable/sensitive to the inputs?

I am trying to predict a continuous 'Y' variable using a variety of algorithms and feature engineering techniques. My issue is that Y is extremely variable and I reached a asymptote in accuracy. This ...
0
votes
0answers
26 views

Building the right prediction process with machine learning

I am working on a two-class classification model to predict if some lead becomes a sale. At this moment I have all leads try to predict these ones, which are sales. I become good metrics right now ...
1
vote
0answers
29 views

Getting a neural network to approximate x^2 [duplicate]

I don't get why it is so hard to get my neural network to learn such a simple function. I've tried all sorts of combinations of layer numbers, number of neurons but it doesn't seem to want to learn. ...
-3
votes
0answers
21 views

which machine learning algorithm would give high accuracy in predicting the movie rating [closed]

I want to predict the movie rating with the help of voted users, profit, FB likes, etc. which algorithm would the best to predict it
2
votes
1answer
28 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 ...
0
votes
1answer
12 views

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.
0
votes
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 ...
0
votes
0answers
10 views

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 ...
1
vote
2answers
120 views

Is there any time series model which handles data at variable frequencies.?

Goal: Predict the yellow points.(yellow events appear at varying frequencies) But I'm struggling to find a good model to fit this use case. Most of the time series algorithms are handling data which ...
0
votes
1answer
103 views

Observation window in Predictive modelling

In any predictive modelling exercise we first start with defining observation window and perform window for the product/problem. Just wanted to know if the window is different for different predictors ...
0
votes
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. ...
2
votes
1answer
135 views

Model building with neural networks

Assume the existence of a collection of physical parameters and a collection of output variables which may depend on the physical parameters. An example in the training dataset consists of a vector ...
0
votes
1answer
44 views

Revenue Projection

Given that we have Monthly revenue data for pass 3 years (36 rows of revenue) We have other data including economic indicators, industry indicators as well (other columns in the 36 rows) ...
0
votes
0answers
6 views

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 ...
1
vote
0answers
14 views

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 ...
0
votes
0answers
193 views

Inverse predict the features from known target with fitted sklearn regressor

I understand that the default way a scikit-learn regressor works is that we fit it to a dataset of features and targets (X_train, ...
1
vote
1answer
39 views

Alternatives for categorical prediction

Upfront question: What are some alternative methods for predicting categorical data? Details: I routinely process data that is 100% categorical. Almost always, this data is nominal (while, ...
1
vote
1answer
29 views

Service Request classification, questionnaire filling and call logging

I am very new to machine learning. I just went through some of the tutorials in Azure and completed one practice workflow(car price prediction). I hope I can ask basic questions here. Scenario : We ...
0
votes
1answer
34 views

How to predict probabilities from a new data set from an already built and validated model in Python?

I have built a classification model using the following steps (and in the mentioned order) in Python - Data cleaning - Removing unwanted variables and separating Predictor variables from response ...
0
votes
0answers
10 views

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 ...
1
vote
1answer
85 views

Negative R2_score Bad predictions for my Sales prediction problem using LightGBM

My project involves trying to predict the sales quantity for a specific item across a whole year. I've used the LightGBM package for making the predictions. The params I've set for it are as follows: <...
1
vote
1answer
49 views

Predictive model to maximize sum of dependent variable?

I am trying to classify cars for a towing company. Junky cars earn more when sent to the junkyard, and the more valuable cars should earn more at the auction, despite the auction fee. Creating a ...
0
votes
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 ...
0
votes
0answers
42 views

Prediction with very less data

Here is a question I am struggling with Training data: ...
0
votes
0answers
15 views

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....
0
votes
2answers
206 views

Keras input for multivariate classification with LSTM using current features and previous timesteps features and y values

I am working on a multivariate binary classification problem. What I want to do is to predict a binary classification given the features at the current timestep and the data (features+real ...
0
votes
0answers
14 views

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 ...
0
votes
1answer
247 views

Include time as a variable in regression model

I am currently working on a regression problem which requires me to predict the costs of a fixed asset. I have used several variables to do so and derived a predicted cost. However, my superior has ...
0
votes
1answer
20 views

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?
1
vote
2answers
73 views

How to approach data prediction problem

I'm new to ML and data science. I would really like high level advice how to approach the following problem. I need to predict if an engine will fail, what I've is a sensor that give a certain value ...
0
votes
1answer
23 views

How do I build a model to predict which service a customer will use on an app?

There's an app with over 50 services. I have the data on the type of service a specific customer (they have a unique customer number) does on the app, the date, location, time, duration on a service ...
1
vote
1answer
25 views

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 ...
0
votes
0answers
21 views

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-...
0
votes
2answers
891 views

What are recommended methods for multi-task prediction?

Currently, we are working on a school project which is trying to predict the number of crimes in some area/neighbourhood. There are 8 different categories for crimes and we've tried to find the ...
0
votes
0answers
72 views

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 ...
1
vote
0answers
19 views

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 ...
1
vote
1answer
32 views

Propensity model with Only Positive Data

Is it possible to build a propensity model (i.e., the likelihood that a user will buy an item) using only positive values. For example, I have a bunch of data about Customers (people that bought stuff)...
1
vote
0answers
11 views

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. ...
3
votes
1answer
2k views

Why is Spark's LinearRegressionWithSGD very slow locally?

I have been trying to run linear regression with SGD that is found in Spark mllib for some time and I am experiencing huge performance problems. All examples that I was looking have number of ...
0
votes
0answers
11 views

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 ...
0
votes
0answers
11 views

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 ...
0
votes
3answers
64 views

Whether Interaction terms should be included in Linear Regression analysis?

I am working on a linear model with 6 independent variables and when thinking about including an interaction I got lost. An interaction exists if the level of one independent variable is affected by ...

1
2 3 4 5
20