Questions tagged [predictive-modeling]

Statistical techniques used for predicting outcomes.

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1answer
165 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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2answers
8k views

How to train model to predict events 30 minutes prior, from multi-dimensionnal timeseries

Experts in my field are capable of predicting the likelyhood an event (binary spike in yellow) 30 minutes before it occurs. Frequency here is 1 sec, this view represents a few hours worth of data, i ...
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2answers
2k views

Machine Learning Steps

Which of the below set of steps options is the correct one when creating a predictive model? Option 1: First eliminate the most obviously bad predictors, and preprocess the remaining if needed, then ...
4
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4answers
3k views

Scikit Learn Missing Data - Categorical values

I have a dataset containing categorical features, which has 4 labels, and 4 features. (It is a meta classifier, so outputs from base classifier serve as input into this classifier) ...
53
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8answers
14k views

Why Is Overfitting Bad in Machine Learning?

Logic often states that by overfitting a model, its capacity to generalize is limited, though this might only mean that overfitting stops a model from improving after a certain complexity. Does ...
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2answers
19k views

Predicting a word using Word2vec model

Given a sentence: "When I open the ?? door it starts heating automatically" I would like to get the list of possible words in ?? with a probability. The basic concept used in word2vec model ...
14
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3answers
13k views

Is feature selection necessary?

I would like to run some machine learning model like random forest, gradient boosting, or SVM on my dataset. There are more than 200 predictor variables in my dataset and my target classes are a ...
3
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1answer
50 views

How data are prepared during training, testing and in production?

Most of real world datasets have features with missing values. Replacing missing values with an appropriate value such as its mean, is considered as a good step in feature engineering. Some times we ...
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1answer
3k views

Predictive modeling based on RFM scoring indicators

RFM - is a ranking model when all customers are ranked according to their purchasing F requency, R recency and M monetary value. This indicator is highly used by marketing departments of various ...
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2answers
2k views

What are the relationships/differences between Bias, Variance and Residuals?

I've been trying to find an answer to this question for a long time. What are the relationships/differences between Bias, Variance and Residuals? I think I do understand Bias, Variance and Residuals ...
48
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5answers
44k views

Should a model be re-trained if new observations are available?

So, I have not been able to find any literature on this subject but it seems like something worth giving a thought: What are the best practices in model training and optimization if new observations ...
26
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1answer
9k views

Time Series prediction using LSTMs: Importance of making time series stationary

In this link on Stationarity and differencing, it has been mentioned that models like ARIMA require a stationarized time series for forecasting as it's statistical properties like mean, variance, ...
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3answers
646 views

Why are ensembles so unreasonably effective

It seems to have become axiomatic that an ensemble of learners leads to the best possible model results - and it is becoming far rarer, for example, for single models to win competitions such as ...
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4answers
26k views

How to avoid overfitting in random forest?

I want to avoid overfitting in random forest. In this regard, I intend to use mtry, nodesize, and maxnodes etc. Could you please help me choose values for these parameters? I am using R. Also, if ...
5
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1answer
4k views

Predictive analysis of rare events

I'm trying to predict rare events, meaning less than 1% of positive cases. I basically try to predict if a subject will have 0, 1, 2 ... , 6, > 6 failures (there are cases in all those categories). I'...
5
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2answers
11k views

Xgboost predict probabilities

When using the python / sklearn API of xgboost are the probabilities obtained via the predict_proba method "real probabilities" or do I have to use ...
5
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3answers
3k views

Best regression model to use for sales prediction

I have the following variables along with sales data going back a few years: date # simple date, can be split in year, month etc shipping_time (0-6 weeks) # 0 weeks means in stock, more weeks means ...
7
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2answers
20k views

How to interpret a decision tree correctly?

I'm trying to work out if I'm correctly interpreting a decision tree found online. The dependent variable of this decision tree is Credit Rating which has two classes, Bad or Good. The root of this ...
5
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2answers
182 views

How to adjust cofounders in Logistic regression?

I have a binary classification problem where I apply logistic regression. I have a set of features that are found significant. But I understand that Logistic regression doesn't consider feature ...
11
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3answers
2k views

Can regression trees predict continuously?

Suppose I have a smooth function like $f(x, y) = x^2+y^2$. I have a training set $D \subsetneq \{((x, y), f(x,y)) | (x,y) \in \mathbb{R}^2\}$ and, of course, I don't know $f$ although I can evaluate $...
8
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2answers
3k views

Is it valid to shuffle time-series data for a prediction task?

I have a time-series dataset that records some participants' daily features from wearable sensors and their daily mood status. The goal is to use one day's daily features and predict the next day's ...
2
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1answer
246 views

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 ...
0
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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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2answers
10k views

How to use Cohen's Kappa as the evaluation metric in GridSearchCV in Scikit Learn?

I have class imbalance in the ratio 1:15 i.e. very low event rate. So to select tuning parameters of GBM in scikit learn I want to use Kappa instead of F1 score. My understanding is Kappa is a better ...
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3answers
344 views

Regression: How to deal with positive skewness in continuous target variable

I'm working on a regression problem. My aim is to "learn" the distribution of a continuous target $y$ as good as possible to make predictions. My model looks like: $$y_i=\beta X_i + u_i.$$ $y$ is ...
4
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2answers
2k views

fix first two levels of decision tree?

I am trying to build a regression tree with 70 attributes where the business team wants to fix the first two levels namely country and product type.To achieve this,I have two proposals: 1.Build a ...
4
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1answer
411 views

How to predict based on multiple samples?

I am relatively new to ML so I apologies in advance if my question shows lack of understating of the field. The problem A particular study course has a high drop-out rate and we want to reduce it. ...
3
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1answer
3k views

Classification using xgboost - predictions

I was trying to build a 0-1 classifier using xgboost R package. My question is how predictions are made? For example in random forests, trees "vote" against each option and the final prediction is ...
2
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2answers
215 views

How to select features for a ML model

I have a dataset with 5K records for binary classification problem. My features are min_blood_pressure, max_blood_pressure, <...
2
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2answers
141 views

Is there any standard pattern recognition algorithm in predicting an item which a user will be buying next, given I have the history of the purchases

I am having a list of 10 different items a user has bought in the past. Each item has been bought multiple times. I would like to find a pattern in which the user buys a particular item and predict ...
2
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1answer
537 views

Which algorithms should I use for recommendation system using a graph database?

Basically I'm developing a recommendation system using a graph database (specifically neo4j), and I want to apply recommendation algorithms. Since i'm using a graph database, I can see the ...
2
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1answer
500 views

model with only positive responses

Could any one help me know about different approaches, methods or algorithms to build a model only with positive responses. Let's assume we have a set of customers with a 'positive' behaviour. We ...
2
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1answer
813 views

How to perform model selection for One-Class Classification?

Consistency based model selection does not perform well for many datasets for One-Class Classification (OCC). So I am looking for some other model selection criteria. Since, only one class of data (...
1
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1answer
107 views

Machine Learning for user modelling

I have a dataset where each row is a interaction of a user with a content. I have user's features to represent the user (each user is uniquely represented through user.id): ...
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3answers
57 views

How to best use geographical information as a factor?

I am trying to predict crime rates and I have naively used lat and long as two separate factors (which seem to work well!). Are there any best practices for location as a factor?
1
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1answer
2k views

Anomaly detection in Time Series Data - Help Required [closed]

I am looking for algorithms on Anomaly detection for time series data. It is uni-variate analysis, considering single parameter (inlet pressure) of air compressor sensor data. The objective is to ...
1
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1answer
33 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 ...
8
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1answer
751 views

Ideas for prospect scoring model

I have to think about a model to identify prospects (companies) that have a high chance of being converted into clients, and I'm looking for advice on what kind of model could be of use. The ...
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2answers
8k views

How to predict customer's next purchase

Suppose we want to predict what customer will buy during his next visit to the Electronic Shop based on his past purchase history. I know that it is a very broad question, but I am new to machine ...
4
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2answers
612 views

Denormalise data in Neural Networks

I wrote a Neural Networks prediction model in Python. My data has a few inputs and two outputs. In order to make it work, I have to normalise every column on data for good prediction results. ...
4
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1answer
8k views

Choosing a model for dataset with categorical variables [closed]

I have a question about the type of model which I should use for a dataset I have. The dataset has a total of 7 independent variables and 1 dependent variable which I need to predict. Out of the 7 ...
3
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3answers
968 views

How to interpret shapley force plot for feature importance?

I am trying to practice and learn shapley value approach to explain my predictions on a binary classification problem. However am having difficulty in understanding the below plot. 1) Does it ...
3
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2answers
111 views

Does Sampling size matters in Multi classification Model

I am working on a multi class classification model where few of the class are with less data compare to other classes. I used random sampling technique to create a sample from the population keeping ...
3
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2answers
21k views

Time series forecast using SVM?

I have a pandas data frame like this: ...
3
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1answer
148 views

How to make machine learning specifically for an individual in a group when we have the data on the group?

Lets specify the question with the help of the figure below: We know that one part of the behaviour (our target Y) will depend on common parameters (for the group). It is represented by the grey zone ...
2
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1answer
749 views

Is there an R package which uses neural networks to explicitly model count data?

Ripley's nnet package, for example, allows you to model count data using a multi nomial setting but is there a package which preserves the complete information relating to a count? For example, ...
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2answers
65 views

How to build a model on a dataset having 40% missing values in most of the variables?

I have a huge dataset of 10 million observations but most of the variables are missing for 40% records. There are couple of variables available for the whole dataset such as sic code(Industry category)...
1
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1answer
47 views

ML Project - Achieve 2 Objectives

I have a dataset with 5K records focused on binary classification. I am posting it here to seek your suggestions on project methodology Currently what is my objective is 1) Run statsmodel logistic ...
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2answers
65 views

Predicting the likelihood that a prediction from a linear regression model is accurate

So to set up the problem: I have a data set that had labeled data like colour, brand and quality as independent variables and the dependent is RRP (price). I have made a linear regression model using ...
0
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4answers
811 views

Time series - is it necessary to retrain the model when new time series data is present

Say you're building a sales prediction model to predict tomorrow's sales value, as well as the next 2 weeks of daily sales. The model is being trained using daily data for the previous 1.5 years, and ...