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Questions tagged [predictive-modeling]

Statistical techniques used for predicting outcomes.

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1answer
19 views

Predict total responses to emails amongst multiple groups

I've got historical data about email characteristics (like time sent, length, topic etc.), and the respondents to these emails - I've got their IP, which is further linked to gender, domicile, ...
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1answer
15 views

Using machine learning or other methods for a fitting problem

I have a time series of data of when 'data events' are going to happen. e.g. 1.2, 4.6, 10.0, 17.3, 23.2, 24.3, 30.6, etc. I am trying to make predictions as to when the next 'data event' will happen. ...
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14 views

Making use of several time series in one LSTM model

I am working on a case where I want to do a multivariate and multi-step time series forecasting. I have hourly data that measures temperature at approximately 500 different devices. (the devices have ...
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1answer
13 views

Where are the predicted results located in this model?

I am new at this and am pretty sure this is a stupid question, but here it goes: Where can I see the results of a model's prediction? I did this course about deep learning, followed the tutorial, ran ...
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1answer
18 views

Forecasting profit based on allocation of labor and time-series data [closed]

Situation: a store sells services A & B, and we have historical data for daily sales/revenue/profit of each service. The store is interested in whether they should staff for more of service A or ...
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1answer
32 views

Selecting Training Set from Model CV Error

What I would like to do is recursively: Train the model on all data Remove the sample(s) with highest error Repeat until the remaining samples have an acceptable error The hypothesis is: "To ...
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1answer
49 views

"Up or down but not sideways" bimodal time series prediction - what is the best way to model it?

Say I have a time series (e.g. bitcoin price). I want to predict tomorrow's price, specifically tomorrow's % change in price from today. Let's say this is gaussian distributed, with the mean at 0%. If ...
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1answer
13 views

What are some deep learning models use in timeseries forecasting that include context from covariates?

I was going through the literature for time-series forecasting using DL and all the methods I read about only use the variable of interest at previous timesteps to predict the same variable at time ...
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1answer
48 views

Logistic Regression to model a rare event

I have a data set which has data on consumers and a flag for whether they have expressed interest in a product or not. I am looking to build a model using R which will be able to predict whether or ...
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2answers
63 views

LSTM model, poor performance

I have been working on a project on the demand for a product. I am using data from 2016 to train the LSTM model. The architecture is as follows: ...
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0answers
19 views

How to avoid Naïve Time Series Forecasting

I'm trying different deep learning models for time series prediction (Bitcoin Price), But the results are too good to Be true and I'm suspecting that the model is just learning to copy previous values ...
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0answers
8 views

Predicting labels greater than those in training set

I have to use a neural network to predict the value of a certain stock on the next day. I'm using an lstm net, feeding it with 7 days worth of data and using the 8th day price as target. I split the ...
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15 views

Qaulity assurance framework for ML/AI

I'd like to hear how organisations are applying a quality framework to ML/AI work. I'm struggling to find good content or good practice on this. By saying quality frameworks, for example, if I was ...
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34 views

How to use time series datasets that have the same time stamps and features across multiple locations for predicting energy output?

I have some datasets: One dataset is comprised of the overall solar energy/wind energy production of a country. The rest of the datasets contain weather data (temperature, pressure, wind speed, etc.) ...
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0answers
13 views

Rule of Thumb for number of observations required to train a model with n independent variables?

I am aware adding more features to a model leads to overfitting of a model. Is there a rule of thumb for minimum number of rows required to build a model with n features in order to build a ...
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14 views

LSTM Data Preparation Input Shape

I have a 2-D dataframe df: ...
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0answers
14 views

OLS Regression: Predicting to the certain total

I have a simple dataset: Rooms Price(in March) Single 20 Balcony 50 Triple 100 Couple 75 Family 150 Now, I can predict the values and set the ...
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0answers
13 views

Time-Series Cross-Validation for LSTM

Is it at all possible to separate my data into train/test sets with cross validation for time series data? I am experimenting with a LSTM model. Also, I am hoping to prevent data leakage/peaking in ...
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1answer
41 views

Labeling and aggregating features issue

I am trying build a simple binary classifier (some tree based algorithm for now) and my training data will have features aggregated at the user level. So I'll have a unique records of each user. These ...
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9 views

Why are the values of my Y predicted the same and R-Squared Negative in SupervisedDBNRegression, Neural Networks

My model is not outputting the results I expected. I don't quite know my way around ANN. After learning how to use SupervisedDBNClassification from https://github.com/albertbup/deep-belief-network I ...
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0answers
39 views

How to do online retraining of model on a single new data point/observation?

I am trying to investigate the effect on performance on old data and new data when a classifier is retrained on only the new observation when it is encountered. The aim is to retrain the classifier on ...
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1answer
124 views

'str' object has no attribute 'predict' [closed]

I am trying to deploy my ML model using flask. My model contains both categorical and numerical variables. Below is my model.py code:- ...
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1answer
35 views

In general, what are precision, recall, F1 that are reported in papers?

I used classification_report in sklearn library And, the picture below shows evaluation on my model (anomaly detector) In general, what are precision, recall, F1 ...
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16 views

Combining output (deciles or risk score) of 2 or more logistic regression based model into one single output SCORE so to use for any decision making

I have 3 different datasets in which customer can or cant be present in all 3 datasets. I have built 3 models for all 3 datasets which are working fine. Kindly note, i cant make a make model ...
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1answer
22 views

Random forest regression model improvement

I am working with vehicle occupancy prediction and I am very much new to this, I have used random forest regression to predict the occupancy values. Random forest jupyter notebook have around 48 M ...
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0answers
74 views

How to reduce/ optimiize a value to make a prediction model?

I have to make a time prediction model with some features. There is certain optimization value for each rows. by reducing that value I can get optimal prediction ( according to the expectations ) How ...
1
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1answer
23 views

What methods are there for predicting a signal?

I have a large dataset of signals (composed of time series). All time series describe the same process, but each series has a different duration (number of points). Based on these time series, I want ...
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49 views

Com posso resolver esse erro: TypeError: a bytes-like object is required, not '_io.BufferedReader'

msg: erro: TypeError: a bytes-like object is required, not '_io.BufferedReader' My code: import pickle with open(b'ModelosParaTrader/ModeloEurUsd.pkcls', 'rb') as modelo: lr = pickle.loads(modelo) lr
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18 views

How can I use a prediction model (e.g., ARMA model or LSTM) for multi-variate data?

I have had a dataset below: sensor1 sensor2 sensor3 ... 2021-01-01 1.32 2.2 1.0 2021-01-02 4.3 2.0 0.8 ... ... I know ...
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0answers
29 views

trusting RMSE or trusting visualized result?

As has been shared everywhere, to see the performance of the model, it is essential to check one of the things such as RMSE, MAE,...
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1answer
12 views

How to interprete the feature significance and the evaluation metrics in classification predictive model?

Consider a experiment to predict the Google-Play apps rating using a Random-Forest classifier with scikit-learn in Python. Three attributes 'Free', 'Size' and 'Category' are utilized to predict the ...
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10 views

Prediction Intervals on (Multi-Step) Judgement Forecasts

Are there any R packages available or general methodologies for calculating prediction intervals on judgment-based forecasts? I've looked at Hyndman's text and the R forecast package - which will ...
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0answers
27 views

Can we add positional encoding to time series input for time series prediction?

I want to use classical machine learning models such XGBoost for my time series prediction. Since the input data for XGBoost/sklearn based models is 2d i.e. (n_samples, n_features), I want to encode ...
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25 views

Feature selections on replicates?

For example, if I have the following data: US = United State, Aus = Australia, MX = Mexico L = Low , M = Middle, H = High (Not lots of variations between them, can consider them as ...
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1answer
25 views

Feature engineering: The more features I add the better RMSE I get?

I have a model with 7 features, I'm trying to figure out if I can improve the performance of this model by adding additional features. So I'm relying on the RMSE to measure the accuracy of my ...
1
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1answer
21 views

RecSys - Large dataset, few resources, how to sample?

I have been working with a RecSys model, for the first time, by experimenting with matrix factorization and matrix factorization with EmbedNN's. However, I am running into a memory problem since my ...
1
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1answer
16 views

Seq to Seq modelling - ML Algorithms to use

Am new to ML. While I learnt the classical ML concepts like Linera regression, Logistic regression, Boosting and tree based techniques, now am slowly trying to learn Deep Learning techniques like CNN, ...
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0answers
13 views

Prediciting outperformance - choice of statistical design?

I want to predict relative outperformance between a stock and an associated benchmark index using time-series models (e.g. ARIMA, LSTM) and some exogenous variables (day of the week, corporate ...
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2answers
31 views

How to choose products based on Number of good, bad and total reviews?

Let us suppose, I have few scenarios for products with good and bad reviews. P1: 1000 Good, 1 bad P2: 100 good, 10 bad P3: 20 Good, 0 bad P4: 10000 good, 500 bad ...
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1answer
25 views

What is the typical things in Data that i have to look for, when implementing Survival Models using Machine Learning?

Problem Scenario I am working on an industry specific problem focussed on predicting the failure of a seal/gasket in the given time interval(T) in a high-pressure-compression environment. Whenever ...
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1answer
27 views

What is the fastest way to run predictions on a big dataset using pandas, numpy and keras?

I am a bit new to the field of data science and could really use some help. I used a natural language dictionary to train and test an ml model using keras and tensorflow. It detects the sentiments in ...
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1answer
23 views

How to classify ordered labels(ordinal data)?

I have some data similar to movie ratings and the labels are ordered, like 1 to 10. since the target label is not a nominal but ordinal variable, what types of models should I be using for classifying ...
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0answers
14 views

Interpreting LSTM Univariate Time Series model

I have built a Deep Learning Sequential Model using LSTM to forecast a variable. How to interpret this model? I want to know which day/hour/season is driving the predictions.
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1answer
66 views

Calculating confidence interval for model accuracy in a multi-class classification problem

In the book Applied Predictive Modeling by Max Kuhn and Kjell Johnson, there is an exercise concerning the calculation of a confidence interval for model accuracy. It reads as follows. ...
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2answers
41 views

How can compare suggestion models with different performances?

I have 4 class binary classification models. That models identify which class a particular students is suitable for. For example, we have user 1 and 4 classes ...
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0answers
21 views

Regression with tensorflow

I am trying to train a tensorflow model for regression analysis using this tutorial: https://www.tensorflow.org/tutorials/keras/regression. My dataset is this: ...
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1answer
25 views

Predicting probability of occurrence using Historical Time Series Data

If you are given 1000 two digits numbers in a row. You have to find 5 most probable numbers that would appear at 1001th place by using historical pattern. (If we can find probability of occurrence ...
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0answers
9 views

Does anyone know of literature regarding a Neural Net boosted GBM?

For obvious reasons, most GBMs created in the private sector are tree boosted. Occasionally, one might want a linear boosted GBM so that the residual models collapse into a simple linear combination. ...
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0answers
20 views

Should predictive modelling response variable be measured in time after the predictors?

I am interested in modelling my dataset in a predictive way to predict the outcome (eg some event occurance) at some time in the future. I have been getting confused whether the outcome variable for ...
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0answers
12 views

Is it possible to predict based on arbitrary attributes?

I have a job/task runner system that takes a task description as input from a queue and delegates it to a machine on which to run. Based on the task type and other properties, some which are task-...

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