Questions tagged [predictive-modeling]
Statistical techniques used for predicting outcomes.
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Multivariate time series forecast with VAR confusion
I am new to time-series forecasting. I am working now on a task in which I have a data set, containing samples of approx. 15 variables for every hour for several years. Then, I have a test data set (...
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1answer
18 views
Can someone explain to me how to use a predictive model to predict something other than the training set
So let's say I create a logistic model to predict who will open a loan based on a based email list that includes who opened and who didn't that's 90% accurate. The model says age, income, bank ...
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31 views
AB testing for Recommender models
Let's say that I have two recommendation system models built, Model A and Model B.
Now I track the performance of both the models for 5 days from 1st Jan to 5th Jan.
Each model has been assigned a ...
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0answers
9 views
Predict variable from time series when there are many observations in the same date
I have dataframe that is similar to this:
...
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0answers
28 views
Predict on new data / Model Deployment
I do not undestand, how the deployment of a ML-model works in the reality.
A given dataset needs to be mostly time pre-processed (for example One Hot Encoding).
After will be a model cretead and ...
1
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1answer
25 views
Training a model with Cross-Validation
I'm training a model with CV and then I'm testing the predictions on a new test set.
Am I doing the right thing or is it necessary to test the predictions on a new dataset using Cross-Validation?
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1answer
13 views
X Second samples taken on unevenly spaced intervals
I have dataset of following specification:
512 samples taken at unevenly spaced intervals over the year
Each sample is an 8 second data from sensors with 4ms resolution
Samples are not labeled
For ...
0
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1answer
48 views
Best way to predict ranges of money?
I am currently working on a project in which I have to develop a model to predict how much money other companies will make by using the services provided by my company. The money made is a type of tax ...
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17 views
I am building a anomoly detection model using Pyculiarity (Twitter's anomaly detection). However, the expected value is 'NaN'. What am I missing?
When I run the following code, I get the expected value as NaN:
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0answers
11 views
ValueError: Input contains NaN, infinity or a value too large for dtype('float32') on predicting case (similar to titanic predicting)
I am still newbie on python with jupyter notebook
I'd like to ask how to solve error "ValueError: Input contains NaN, infinity or a value too large for dtype('float32')"
first I make ...
-1
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1answer
29 views
Forecasting with Neural network and understanding which underlying model is favoured
If I have a very large set of data (~ 1TB). How can I use Neural Network on this data to understand which underlying distribution (eg. let's say a Gaussian or a Poissonian with a certain mean, sd) is ...
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0answers
13 views
What is a good way to handle nominal spatial data with a changing number of categories to use in prediction model?
For a project I'm going to be working with spatial data with a nominal attribute (land use). Every year the number of categories for this attribute changes because categories split or merge. I do have ...
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1answer
16 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 ...
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0answers
22 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.
...
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2answers
22 views
Problem with Logistic regression using categorial variables
I have a problem with Logistic regression using categorial variables. This my training datasets :
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0answers
43 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
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1answer
18 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 ...
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2answers
30 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 ...
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0answers
30 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. ...
2
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1answer
32 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
13 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.
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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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0answers
7 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 ...
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0answers
12 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 ...
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1answer
29 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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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 ...
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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....
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42 views
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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 ...
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1answer
22 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?
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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 ...
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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-...
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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 ...
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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 ...
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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. ...
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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 ...
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12 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 ...
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0answers
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)....
1
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1answer
19 views
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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0answers
9 views
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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0answers
21 views
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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0answers
26 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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1answer
48 views
1
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1answer
43 views
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 ...
2
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0answers
19 views
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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21 views
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 ...
0
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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 ...
