Questions tagged [predictive-modeling]
Statistical techniques used for predicting outcomes.
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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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1answer
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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1answer
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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0answers
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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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 ...
2
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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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2answers
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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2answers
31 views
Data with similar mean, min and max across all columns. What could I do to build a classifier
I have a data with the following columns
...
2
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2answers
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 ...
2
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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 ...
