Questions tagged [predictive-modeling]

Statistical techniques used for predicting outcomes.

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48 views

Visualization of transformed features in BERT

So I'm trying the Intent Recognition with BERT using Keras and TensorFlow 2 available at kdnuggets.com and this is the code for the results evaluation. ...
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90 views

What's the order in applying SMOTE transformation in a pipeline?

Here's the thing, I have an imbalanced data and I was thinking about using SMOTE transformation. However, when doing that using a sklearn pipeline, I get an error because of missing values. This is my ...
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142 views

Hyperparameter Tuning in Random Forest Model

I'm new to the machine learning field, and I'm learning ML models by practice, and I'm facing an issue while using the machine learning model. While I'm implementing the ...
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1answer
60 views

Neural Network regression negative performance

I have a problem with the performance of a multi layer perceptron regressor (neural network) and I cannot figure out why. Task: I am trying to improve a time series prediction. I have predictions of a ...
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42 views

Time series stationarize vs normalization

I have multiple time series coming from sensor measurements of an industrial machine. The industrial machine runs different 'Recipes'. Every recipe has different set of parameters which are set before ...
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23 views

Trying to figure best machine learning technique to evaluate schedules for a telescope

I work for an astronomical observatory with two geographically separated telescopes. Users submit proposals in the form of programs consisting of multiple observations, and we are trying to automize ...
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21 views

How to incorporate features available only at training time into my model

I'm building classifier with a binary label. In my particular dateset the training data has many more features than the test set, in fact most of them are not available in the test set (beyond my ...
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1answer
67 views

How to use a multiple linear regression model built from normalized data

I built a linear multivariable regression model from normalized data (for the interval [0; 1]). Initially, the data was not normalized, I normalized the data by myself (independent and dependent ...
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53 views

How to use future holiday/promo days as input in multivariate LSTM sales forecasting

Im building a forecast using an LSTM in tensorflow 2. My data consists of 7 columns: date (daily), gross_sales (the target), daily_total_inventory, avg_daily_order_value, daily_total_new_customers, ...
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22 views

How do I deploy a model when using Stratified K fold?

I have used Stratified K fold for learning the model . Below is the python code: ...
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17 views

How to validate the model when using Smote technique? [duplicate]

I am using Smote to treat Class Imbalance and I apply Smote only on the train data. I fit a model using balanced data("Smoted" data). Now I need to check recall and precision for train and ...
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14 views

How to implement various SVR types in python?

I am new to the Machine learning world and have created some models on wind speed prediction using SVR and RF. Now, I wish to implement and compare these results with LSSVR (Least square support ...
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46 views

How to compare 2 datasets with machine learning

I have multiple datasets from different sources (multiple cities) but with the same parameters and one output that I want to classify. Each dataset has different number of datapoints (thousands). I am ...
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25 views

How to incorporate the “current time” into an parcel ETA prediction model

I need to create a model that is able to predict the ETA of parcels, using only discrete events about when and where the good was last processed. For each parcel I have a sequence, of varying length, ...
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1answer
161 views

Removing constant from the regression model

I am trying to calibrate two variables $(X,Y)$ of different measuring techniques from two instruments, the result of the linear regression analysis appears as shown in the image. The result shows the ...
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1answer
24 views

ElasticNet Convergence odd behavior

I am optimizing a model using ElasticNet, but am getting some odd behavior. When I set the tolerance hyperparameter with a small value, I get ...
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45 views

Poisson model with overdisperssion

I'm working with a dataset $X$ (of length $N$) of count data, which looks like: I developed a statistical model which can be improved, so I'm asking for any suggestions, for instance, differnet ...
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2answers
77 views

How to predict when an appointment will be scheduled?

I have a dataset of tens of thousands of appointments. Appointments have a created date and scheduled date. Something like this: ...
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2answers
41 views

Image multi class classifier CNN

I have a problem, im designing a multiclass classifier to classify medic images, I have to classify in which grade of desease is it, this are 6 grades , each time the joint deforms a little, so, mi ...
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2answers
64 views

Low precision on classification model

I am working since some months on a prediction from lead to a sale. Someone makes a lead on my website and I want to predict if this user will make a sale. I have these metrics on the test data. Now ...
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1answer
24 views

Predicting time series data

I have a dataset as following: This is test case 1. My goal is to fill the missing years data. As the age sex and smoking is not changing so I have to predict the condition and percent data for year ...
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1answer
62 views

Understanding one of the assumptions of linear regression: Multicollinearity

I've read that multicollinearity is one of the main assumptions of multivariate linear regression - Multicollinearity occurs when the independent variables are too highly correlated with each other. ...
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1answer
50 views

High probabilities of success for wrong predictions

I'm studying the behavior of machine failures in a production scenario. For this, I generated random data to form my imbalanced training set, consisting of categorical data, which indicate whether or ...
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1answer
47 views

Data model with more outputs than inputs?

I am working on parametric studies in physics simulations, i.e. I vary some real input parameters (e.g. x0,x1,x2,x3) and get an output with a larger size (e.g. y0,y1 ... y100). Assuming that I have a ...
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1answer
62 views

Is it possible to forecast the evolution of cars?

Let's say for example that I have a dataset about the cars that a company (e.g. Toyota) produced, over the course of the years 1990 - 2016. Considering that I have already completed the feature ...
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1answer
21 views

Determining which categorical data is beneficial in predictive modelling

I am working on a model which will allow me to predict how long it will take for a "job" to be completed, based on historical data. Each job has a handful of categorical characteristics (all ...
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1answer
29 views

Model for predicting duration based on categorical data

I am working on a model which will allow me to predict how long it will take for a "job" to be completed, based on historical data. Each job has a handful of categorical characteristics (all ...
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56 views

Predict how many days until New Purchase

I am trying to introduce myself into predicting with dates and I am having big troubles understanding how to do it. I have a dataset with customer orders from an e-commerce platform with the usual ...
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1answer
51 views

How can we identify potential customers for a new list of customers?

I have two data sets: Customer demographic data; Transaction data of the customers. Now, if I have to identify potential customers to develop a marketing strategy, I would make use of clustering to ...
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36 views

Custom Loss Function for Mixing Sparse and Dense Features for a Prediction Problem

I have a largely uncorrelated feature space of about 40 dichotomous features, using which I'm trying to predict a continuous target variable. Now, some of these features are very sparse (Active less ...
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11 views

Predicting a variably-placed value in a vector

I have $m$ vectors in $\mathbb{R}^n$, where $m >> n$, and I want to train a model to impute a value $x_i$ in $\mathbf{x}$, where $1 \leq i \leq n$ (and can vary by vector). For instance, I may ...
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14 views

Predicting next day value

I am new to Data Science and I am trying to solve this problem. It is a problem of supervised learning. I have a dataset that for every day of a time interval, for every defined geographic point, has ...
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2answers
130 views

How to model user choice probability: binary model vs multi class model

Let's say Morpheus has multiple users to offer colored pills(from an infinite set of colored pills), there are in total 3 unique colored pills(red, blue, green) Morpheus can offer. The trick is, ...
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3answers
47 views

how to use predictions on a single value?

I am comfortable using Machine learning on my train data and test data and validate it. But the question here is if I want to predict a single variable how do I do it? Let's suppose I have done ...
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2answers
489 views

Best common metric for comparing classic time series forecasting methods (ARIMA/Prophet) with ML approach?

I am new to time series forecasting and looking to compare the performance of ARIMA/Prophet with an XGBoost model in predicting future stock market values based on historical stock market data and ...
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2answers
34 views

Train/Test dataset and model [closed]

I would like to ask you how to work on train and test dataset. I have unlabelled data. They are short text (max 100 characters) and I would need to understand their sentiment. To do this, I am ...
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1answer
121 views

Modeling price vs demand

I have a dataset consisting of products, clients, price policy, discounts, quantities, and net sales. The task as put in words by the business is quantity vs price. I have noted a few observations ...
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187 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, ...
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13 views

Using Vector Auto Regression for multiple time series at once

Say I have a dataframe like so: ...
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1answer
106 views

1st order Taylor Series derivative calculation for autoregressive model

I wrote a blog post where I calculated the Taylor Series of an autoregressive function. It is not strictly the Taylor Series, but some variant (I guess). I'm mostly concerned about whether the ...
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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 ...
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2answers
93 views

Checking linearity for a linear regression model?

I've read that there are various assumptions associated with a multiple linear regression model which you should check/validate before getting too excited about your model results. One of these is the ...
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163 views

Stacking and Ensembling methods in Data Science

I understand that using stacking and ensembling has become popular, and these methods can give better results than using a single algorithm. My question is: What are the reasons, statistical or ...
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31 views
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43 views

Text Classification : Classifying N classes vs rest of the classes

Apologies if this is naive, I am fairly new to the domain. I have a requirement where I am trying to classify 2 types of text data, i.e, I have got 2 classes to classify my data upon. I am able to get ...
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1answer
32 views

Best Approach to Forecasting Numerical Value Based on time series and categorical data?

Consider a dataset of thousands of car repairs that have been performed. In simplest of terms, the columns to consider are the time of year when it was broken (seasonal changes in demand for car ...
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1answer
15 views

May the training sets of two predictive modeling cases (with an overlap in features) be combined?

Say we have dataset D1 (columns A, B, C) and D2 (columns A, B, D) with target variable E. As both datasets are rather small, their respective predictive models do not perform really well. To improve ...
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1answer
30 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)...
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2answers
69 views

Predict time to reaction (chemical engineering)?

I need a hint on the problem below. This is related to predictive analysis and chemical engineering. I don't background in chemical engineering, and that's why I am looking for some hints. I want to ...
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1answer
49 views

Ingredients, Recipes and recipe ratings. I would like to predict the rating based on combination of ingredients

I would really appreciate some help on the first steps to my problem, suggestions of modeling techniques i could use or relevant research (i could not find any). I have a list of ingredients (150 in ...

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