Questions tagged [prediction]
prediction is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.
442 questions
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Can Polynomial Features Be Used in Logistic Regression and Random Forest Models?
I am working in Python to predict the treatment response of 43 patients using 10 predictors as input. I noticed that adding polynomial features to my models produces nearly perfect results.
I am ...
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Enhancing the predictive capability of traditional node-similarity indices with the Node2Vec algorithm
I am trying to test enhancing the prediction capabilities of traditional node-similarity algorithms, like the Jaccard Coefficient or Adamic Adar, with graph embeddings, like the Node2Vec. I think it ...
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When the regression models outperforms naive method?
I followed from this question.
Case1:
I have the following task to do: Training by the consecutive 3 days to predict the each 4th day. Each day data represents one CSV file which has dimension 24x25. ...
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Multivariate linear regression via scikit and statsmodels
want to preface this first with terminology: multivariate regression deals with the case where there are more than one dependent variables while multiple regression deals with the case where there is ...
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Need advice on feature engineering on Longitudinal Data
I'm trying to predict the rated capacity of a wind turbine given factors such as wind speed and direction. Now since this is weather data which is high resolution, I don't want to just average things ...
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Daily Balance Prediction Using LSTM & ARIMA
I have the daily transaction history of a person from 1/1/2022 to 6/24/2024 in a csv file. The data is divided into train (1/1/2022 to 5/25/2024) and test (remaining). The data is given as :
Date
...
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Is it appropriate to utilize LSTMs for multivariate binary prediction on a timeseries by sliding block-by-block vs row-by-row?
I am trying to implement an ML algorithm for multivariate regression on a list of several timeseries. There are hundreds of timeseries, each one millions of rows long. There are 13 features, and I'm ...
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How to adjust classification totals based on known bias of estimator
Let's say I have a dataset, $D$, with known ground truth labels. I nonetheless use a few-shot LLM classifier on this dataset to predict $k$ classes for each label.
From the LLM results, I get ...
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This is a Proposed course
The following is a proposed class exercise for a course introducing automated data-driven decision-making to new engineers, given their interest in the topic.
I'm interested in feedback. Does the ...
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predicting categorical result
im doing a clinical research for surgery. Main goal is to predict difficulty of operation with preoperatiive information. I found three variables as significant factor for difficulty of operation ...
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Techniques for solving the problem with an unbalanced data set
I am trying to solve a problem with an unbalanced data set. I have two classes, one is for patients with risk (1), the other for patients without risk (0). I have a larger number of patients without ...
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Calculating prediction confidence from a sequence of token-level confidences
I am working with OCSR (optical chemical structure recognition) models, and they output a sequence of token-level confidences. I am looking for a method of summarising these token-level confidences ...
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saving ML models with pickle to be deployed using Flask
I trained some ensemble Ml to predict, I needed to save with pickle so as to be able to deploy using Flask. To save with pickle I have tried several methods and read several articles but could not get ...
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Build a Neural Network for Multi-output Regression
I have a network model that accepts about 25 inputs and outputs 3 actions.
The outputs are: delta X and delta Y of the robot and the angle of the robot.
After I enter the data into the model, I get ...
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Missing data in train set and test set
I have a dataset of N columns. Now I'm able to preprocess data and find a subset of features that I can use to train a model and make predictions. In the case where the train data has missing feature ...
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Machine Learning Algorithm for identifying the factors contributing to academic performance of students
I have a dataset with several qualitative and quantitative attributes, including age, location (longitude, latitude), city, parent occupation, family size, GPA etc.
My task is to find the attributes/...
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Some questions about Ensemble batch prediction intervals (EnbPI) algorithm
On line 18, should j not start with t-s+1 and end with t? On line 19 why is the same x_t considered in the loop?
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Creating a rule based approach to identify Stock Out products
I am trying to build a Stock Out Predictive Model based on input variables such as:
Sell In Unit: Units per month sold to wholesaler by manufacturer
Sell Out Unit: Units per month sold by wholesaler ...
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is Sequences, Time Series and Prediction course up to date?
Recently I asked a question regarding Time series prediction and someone commented that I should consider taking DeepLearning.AI course: Sequences, Time Series and Prediction.
My goal is to make good ...
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Getting actions probabilities instead of an unique prediction in Stable Baselines 3 SAC?
I try to understand how getting an actions probability table instead of an unique prediction in stable baselines 3 SAC in order to override 'predict' method to filter invalid actions. I guess the good ...
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Sale Forecasting Problem -- Is it legit to use inventory level as a feature?
I'm working on a project to predict future sales for our company's products so that the supply chain can have better idea how much to restock.
Detail about the model I'm working on:
Model: LGBM (from ...
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Prediction with optional parameters
I want to build a machine learning model that has 25 input feature and two labels (my problem is a regression task if it makes any difference), to my knowledge since I have two labels I need two ...
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Using simple RNN to identify a simple dynamic linear system
I have been trying to identify a simple linear second order system (e.g. a pendulum or a mass-spring system), by simulating it in Python using backwards-euler method and then feeding the step changes ...
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R, Caret Predict(), and errors
So I've been playing around with/testing out a few ideas. I just wasn't sure though if there is a simple command you can use to have caret like send you back a list (or data.frame) of all the errors ...
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Winter Holt's Time series model
I am confused with the Winter Holt's Time series model usage. I use 2 years of data to train and want to predict 3rd-year data.
Note1: I have partial 3rd year's data, but I want to use it to check my ...
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411
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Prediction of the next number 0 to 9 that doesn't have a particular sequence
How to predict a number from 0 to 9 that are random and doesn't have any particular sequence that the numbers are following?
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114
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Dealing with varying predictive horizon
I know that the predictive horizon is the time window that runs from the observation of the data to the manifestation of the target variable.
But how can I deal with prediction if the time horizon ...
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Feature selection - How to identify the best subset
I am using three feature selection method on a dataset containing 15 inputs. I need to extract the best 5 features. Each of the three method gave a subset of the input dataset, but they are different. ...
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What is the highest possible prediction accuracy when I flip some labels at random?
I want to predict MNIST labels in a binary setting using a simple MLP model (0 for digits 0-4 and 1 for 5-9). For the train and test data, I randomly flip 25% of the labels.
Is the maximum achievable ...
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Do prediction intervals for Random Forest predictions are average of prediction intervals of trees being Random Forest estimators?
I am working on adding prediction intervals for each prediction value of new input samples.
Do prediction intervals for Random Forest predictions are average of prediction intervals of trees being ...
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ML with Python - extract information from user text
I'm a web developer, got a little experience with Python but none on ML.
Tour operator customer want to introduce AI/ML on his website, the goal is to have a single text input where user can prompt ...
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Sequence prediction in Parent - Child dataset
We have a large collection of documents (D), each accompanied by a set of metadata (M). Within this collection, some documents act as parent documents and have multiple child documents. Both parent ...
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How to predicting the next date with Python ML
I have a list of dates around 10 dates in asc order. These are the dates a buidling was open. I need to predict the next date using this. I tried scikit learn like below
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How to predict what someone will order?
Suppose Prof. X goes to a road side tea-coffee shop everyday at 5pm just after his office. After reaching there he tosses a coin, and places his order tea or coffee. The shop owner Y has been ...
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Remove Seasonality before applying SARIMA model on weekly data?
I am trying to predict average weekly stock prices for time series data.
Steps I followed:
I tested the data to check whether it was stationary or not using ADF ...
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Integrating time context in a machine learning model
Basically, what I'm curious about, are there any methods in machine learning to make the model take into account events that happen in real time that affect the data points during that time period. ...
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Do models of social systems suffer from prediction drift?
Background
I've created a binary classification model that predicts the probability of fraud for a given sample.
The choice of threshold allows me to set how many frauds are captured in the training ...
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Differential equations, real time measurements of variables, and ML
TL;DR: We measure variable $x$ every $10$ minutes, solve a differential equation $\frac{\mathrm{d}y}{\mathrm{d}t}$ where $y=f(x)$. We are interested in the time it takes for the cumulative value of $y$...
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Testing RANSAC regression model
I am going to build the model (e.g. multiple linear regression) to predict the appartment cost in my city. First I have to find outliers in training data. For this task RANSAC regression algorithm ...
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Force network to weigh specific variables during learning
I have a pandas data frame containing around 100000 observations of plant species and their age with additional numerical predictors (climate). I used tensorflow ...
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Combine machine learning feature selection with time series
I have basic knowledge in time series prediction and supervised/unsupervised machine learning algorithms (clustering, classification, decision tree, etc.) I am now given a task to predict a bunch of ...
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How to improve pytorch network to be more precise
I have a pytorch neural network which is being trained on data containing multiple financial values and some other non numeric values translated to numbers with self created logic (for example if ...
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Append prediction of tensorflow to a pandas dataframe
I built a tensorflow model to make text classification in four category, after testing and evaluating it, I need to apply it to actual data to predict the class of them, I create a predict function ...
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Get dependant probabilities in multiclassification
After training my CatBoostClassifier model I call get_proba function which returns me list of probabilities. The problem starts from an another point... I transfer that data into dataframe then to ...
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Excessive features in an Neural Network model
I am modeling a quality parameter of a chemical process product. I have a list of circa 400 process parameters sampled throughout the process. Most of them should have no meaningful impact on the ...
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How to improve the AUC scores of my link prediction algorithm?
I am exploring a link prediction algorithm, but my AUC scores change every time I run it and it is always very low. It varies from 50 to around 72. I was wondering if there is a way to improve their ...
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Strange behaviour around zero for predicted distributions from a deep learning regression model
Can somebody help make sense of these very odd distributions that I obtained from my trained deep learning regression model?
The model was trained with either MAE or MSE loss, which is what the ...
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What model to fit to call center data
I have a dataset with calls from day 1 to day 340. What model can I fit to mathematically capture the pattern?
There are only 1 or 2 digit number of calls on all days except day 61.62.63 and 121.122....
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"Most forecasting algorithms assumes that each point is independent of one another." If so, how forecasting is being possible?
In stationary, if we want to make forecasting, we have to make our data stationary(On classical methods), I get that, but If every data point is independent of each other, how can we make the ...
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