Questions tagged [predictive-modeling]

Statistical techniques used for predicting outcomes.

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How to build a predictive model on normalized data

I have a dataset of accounts that contain a number of users that share a subscription to a product (think Netflix family account or something like that). In order to predict whether an account will be ...
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530 views

when can xgboost or catboost be better then Logistic regression?

I need to improve the prediction result of an algorithm that is already programmed based on logistic regression ( for binary classification). I tried to use XGBoost and CatBoost (with default ...
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Machine learning models for predicting good machine settings given bad machine settings [closed]

I’ve a dataset about a machine that produces a product. Salient features about the dataset: • For each data point, some of the features are machine settings (calibration factor, grind time, ...
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Qualitative prediction based off data containing mixed qualitative and quantitative values? [closed]

I'm trying to find a way to predict an integer value based off of an item's prior sale history and am inquiring for a starting point on how to approach this. The factors are as follows (Data Type in ...
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1answer
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What is the best way to predict multiple outcome from a single entity?

Let's say i have three model: Facial recognition, Face landmark detection, Emotion recognition. Now if i want to predict those three feature from a single image. What should be my approach? Should i ...
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How to solve a classification problem when the independent variables/covariates/feature vectors form a time series?

Cross posted here: https://stats.stackexchange.com/questions/389189/how-to-solve-a-classification-problem-when-the-independent-variables-covariates, but no answer; hence trying here as well! I hope ...
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About applying time series forecasting to problems better suited for reinforcement learning, like toy example “Jack's car rental”

"Jack's car rental" is an example of a reinforcement learning problem, proposed in the Sutton & Barto book, in which the goal is to optimize the daily distribution of cars in two locations of the ...
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Sports prediction using machine learning [closed]

I am trying to predict soccer scores using past results. The dataset I have only consists of the home team, the away team, goals scored by the home team and goals scored by the away team in each match....
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Job Recommendation System

I am building a Job Recommendation System where I have Student Data for different subjects in Machine Learning(Data Viz, Python, Statistics, etc) and their skills from the resume. Need to Recommend ...
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Using LSTM's on Multivariate Input AND Multivariate Output

I have a very small dataset, only about 40 rows, that has historical usage data for a few categories (roughly 20). I strongly suspect that these categories are dependent in a partial-zero-sum-game ...
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Specific data formatting techniques for discontiguous time series?

I'm facing a predicting problem for food alerts. The goal is to predict the variables of the most probable alert in the next x days (also any information I could get about future alerts is really ...
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Anomaly Detection: Model Creation & Implementation

I'm trying to determine the best approach to an anomaly detection problem. Particularly around setting up the data, building the models, and leveraging the models to identify important information. ...
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77 views

Machine Learning in real time

I am a newbie in ML world, but very curious and enthusiastic about it. Have gone through articles and some hands-on too. Still got a silly doubt. In sample datasets (like Iris or diabetes or breast ...
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Mapping 4 Dimensional array to predicted output text

Iv been studying machine learning but im struggling with some concepts and cant seem to find particular answers to the question of how theoretical data is mapped into non classification categories. ...
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1answer
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ML - Service Desk classification

I'm trying to explore an use-case in ML but stuck at a point. May i please request your advise please. Have a service desk web application for logging tickets, which is essentially a form having ...
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Bayesian Neural net with non probibalistic Data?

Is it possible to construct a Bayesian Neural Network without Probability Distributions as dependent Variable for purpose of predictive modeling? I mean, if id like to Infer on a Specific Value, like ...
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1answer
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How to do Predictive Modeling for Different Companies

There is Company X Company X wants to predict if its opportunities will Win or Lose I trained my model based on the data of company X and did the prediction of new opportunities Now Company Y and ...
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2answers
52 views

model to predict annual outcome based on previous years data

I have below datasets for two years each holding about 10.000 records. Every week a new report is generated that shows the performance for the current or any previous month. Therefore a more recent ...
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How to improve a model with a high cross validation score yet with low accuracy on unseen data?

I have a model that is based on an experiment collected on 100 subjects. We are testing the model as follows: Record raw data from the subjects For each subject, compute the feature from the raw data ...
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1answer
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How can we show that one model might have higher accuracy than another model but at the same time lower AUC?

Assume that we have two classification models M1 and M2 that are evaluated on five test instances. How to show with an example that M1 can have a higher accuracy than M2, while at the same time M2 has a ...
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61 views

Label construction for predictive maintenance

I am trying to do a binary classification related to predictive maintenance. The question I address is "What is the probability that the asset will fail in the next X units of time?" There is a guide ...
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42 views

How to figure out if the problem is solvable by any machine learning algorithm?

I am trying to solve a problem in the domain of the audiology, to predict the value of an audio gain in dB. My data is made of: 10,000 training samples 1,000 validation samples 10 features The ...
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1answer
355 views

Predict the loss amount for insurance policies using the historical trends and features?

Anyone who has worked with insurance policy datasets, please guide me to an appropriate dataset for this problem . Also, what is this 'loss amount' and what model will be appropriate for it?
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1answer
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How to merge personalized models together

Let's say I'm building an app like Uber and I want to predict the user's most likely destination based on the user's past history, current latitude-longitude, and current date and time. We have ...
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2answers
293 views

Can we predict when an event will occur in the future from time series data?

I would like to predict a few possible times when a particular event may occur. For instance, I have the daily activity data of a person that consists of what the person doing and when over a period ...
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1answer
755 views

How to calculate prediction error in a LSTM keras

I have an LSTM which I have constructed and run in keras using python. I use this model to predict $n$ points into the future for a time series forecasting problem. When I use a method such as ...
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1answer
35 views

Historical weather data with machine learning? [closed]

My company gave me a task to build some weather forecasting. I have now historical weather data for 10 years (temperature, precipitation in mm, humidity and etc. more than 30 features total). We need ...
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2answers
117 views

When to build separate models

I'm pretty new to predictive modeling, but am interested in generating predictions for credit card account spend. These are existing accounts. The data I have available to me is Card Type (i.e. ...
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How to attribute variance to an input parameter?

Some data Maybe this is easiest to explain by going straight with the data. Here is how much money Bob has at the end of each day. ...
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1answer
53 views

RNN package and problems with “Predictr”

I have two questions about how to use R's RNN package, specifically the trainr and predictr functions. Let's suppose I have a time series of 4000 steps for 5 different variables. How should this be ...
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1answer
73 views

How to train a model to predict a time window than an event will occur on a website

I have a list of historical timestamps of when a specific event occurred on a website. Currently the timestamp represents a 30 minute window that the event happened within. I am looking to train a ...
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56 views

How to find out bad images?

I have a task to find out bad visuals from given video. Let's define bad images in the video as per the human brain -: ...
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1answer
68 views

Can random forest algorithm provide customer churn prediction probability at each customer instead at class level?

I have customer training data set from telecom industry along with its test data set containing churn values 0 & 1 for each customer. I also have customer data set whose churn value is to be ...
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1answer
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During a regression task, I am getting low R^2 values, but elementwise difference between test set and prediction values is huge

I am doing a random forest regression on my dataset (which has abut 15 input features and 1 target feature). I am getting a decently low R^2 of <1 for both the train and test sets (please do let me ...
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175 views

What is the difference between SVM and logistic regression?

While reading the book by Aurelien Geron, I noticed that both logistic regression and SVM predict classes in exactly the same way, so I suspect there must be something that I am missing. In the ...
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1answer
46 views

How to get the survival duration prediction for each individual in the data by using the Kaplan-Meier method?

I am trying to learn how to use the Kaplan-Meier survival estimator model in the lifelines package. The documentation says that the KaplanMeierFitter.fit function ...
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2answers
75 views

Forecasting/predicting techniques for qualitative data?

I have a food alert dataset composed of nominal qualitative variables, such as type of alert, country of origin, action taken, etc. as well as the date on which the alert was recorded. What ...
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1answer
27 views

How to predict unknown(hidden) value by incomplete value or partly recorded value

Let me make it clear by make an example: Suppose I knew a person's cost each month for 3 years like: 2016Jan : $2500 2016Feb : $4000 2016Mar : $3500 ... Just according to this, can I ...
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32 views

Finding interaction

How do you find the interaction between a continuous and a categorical variable? I have tried using ggPredict but it doesn't seem to work if there are more levels. ...
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178 views

Logistic regression cost function

In Aurelien Geron's book I found this line ...
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1answer
43 views

What could be a dataset in which the presence of an outlier dramatically affects the performance of Ordinary Least Squares (OLS) regression?

I am tasked with giving an example of a dataset in which the presence of an outlier dramatically affects the performance of Ordinary Least Squares (OLS) regression. I've searched and searched the web ...
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Why is my regression predictions values variance different from real values variance

I am modeling a physical process using a regression(XGBoost). I'm looking for ways improving my model, have tried different things without success. Decided to get a better intuition on where my model ...
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1answer
29 views

Prediction: plugin a corelation table (neuron) into a Time-Series Neuron in Keras/ TF

i am adding more details I have a time series of Babies (1,2,3) showing how many problem they have each week (Born week 1 to week 80) and in which organ (14 organ). There is a separate numeric time-...
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Is linear regression on the trees of XGBoost (rather than taking their mean) useful/popular?

Given training data $(\underline{x}_1, y_1),...,(\underline{x_N}, y_N)$, one can choose a variety of ensemble method for trees. These algorithms output a set of trees $T_1, ..., T_n$, and then the ...
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2answers
196 views

Why does balancing the test dataset improve precision-recall curve?

I have a fairly imbalanced dataset for default-risk credit scoring (2:98). Both costs are fairly important i.e False negative means loss from default and false positive is a lost-revenue opportunity. ...
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240 views

Can Precision-Recall be improved for imbalanced sample?

I tried out a few models on a highly imbalanced sample (~2:100) where I can get decent AUC from ROC (test sample). But when I plot precision-recall (test sample), it looks horrible. Kind of like the ...
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71 views

Test predictive power of classes in multiclass classification

How can we confidently identify if the data has lower predictive power. I saw that the classifiers are giving poor accuracy (< 50%) on test data and approx 97% on train data. Correlation of a class ...
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76 views

When creating a classification model, should predictors with little correlation to the response variable be included in the model?

I am building a predictive model designed to predict attrition within my organization. I am trying to decide whether to add certain predictors to my model. I used a Kruskal-Wallis rank sum test to ...