Questions tagged [logistic-regression]

Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression

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Precision score (classification_report) 1/0 not predicting for 0 in logistic modeling using Sklearn [on hold]

I have a binary outcome for an imported CSV file I am working with. I used the sklearn library to use the logistic model to gather predictions for my target value and also generate probabilities. ...
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High accuracy in one v. all, lower accuracy in all vs. all

I am training a classifier (similar to logistic regression) on MNIST. I have 10 one -vs.-all classifiers for each number, each of which independently achieves >90% test set accuracy. However, when I ...
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1answer
17 views

How does the given data gets plotted on a graph

I come from a programming background and learning the math behind the data science and algorithms now. I would like to understand the logic behind how a data gets plotted in a graph when using ...
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+50

References for longitudinal data analysis

So my goal is to study longitudinal data (data in time series) by applying some data mining techniques. Ultimately I want to be able to "predict" outcomes. For example, a study of patients along the ...
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28 views

How to improve accuracy beyond optimal level? [on hold]

This may sound like an impossible task, but I believe it's possible in many cases. I have a tabular data set, with 341 columns representing independent predictor variables, and one column with the ...
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How does a simple logistic regression model achieve a 92% classification accuracy on MNIST?

Even though all the images in the MNIST dataset are centered, with a similar scale, and face up with no rotations, they have a significant handwriting variation that puzzles me how a linear model ...
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1answer
19 views

Accuracy noise patterns during model training

I'm training a logistic regression model on a small dataset. I have about 1300 samples that I split into a training and a testing set (70% and 30% respectively). The training seems ok, however when I ...
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1answer
18 views

Why the Logistic regression model trained with tensorflow performed so poor

I trained a logistic regression model with tensorflow but the accuracy of the model was so poor (accuracy = 0.68). The model was trained using simulated dataset and the result should be very good. is ...
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1answer
29 views

Is it compulsary to normalize the dataset if doing so can negatively impact a Binary Logistic regression performance?

I am using raw data set with 4 feature variables to do a Binominal Classification using Logistic Regression Algorithm. I made sure that the class counts are balanced. i.e., an equal number of ...
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Linear/Logistic Regression for unknown values or how to get a good prior for new coefficients

Suppose, we model the probability of making holidays by country and town. The input data are people and how many people actually made holiday in that particular town: ...
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1answer
61 views

How to interpret Logistic regression coefficients using scikit learn

I have created a model using Logistic regression with 21 features, most of which is binary. I created these features using get_dummies. Few of the other features are numeric. I get a very good ...
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How do classification algorithms assign coefficients or importance to a categorical feature

I have a binary classification dataset with target variable being response (Y/N) and one of the predictor variable is month. Image has data on respondents count and rate by month (sorted by ...
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Code line explanations: Visuals of LogRegression

Why do we use -1 and +1 in the following code? ...
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1answer
32 views

What do we learn from training a dataset for logistic regression

What do we learn from training our dataset in Logistic Resgression? Like in Linear Regression, with the help of training set we are able to generate a best fit line(y = mx+c) where m and c come from ...
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Which classifier performs better when using 'class_weight'?

I have used the 'class_weight' method to balance my multi-class classification problem, using Logistic Regression, Random Forest, and XGBoost classifiers. Among these three methods, logistic ...
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1answer
24 views

Calculating Univariate and MultiVariate Logistic Regression with Python

I have a simple data set of a number of variables and a single binary dependent variable. The data is stored in a data frame. When I use python's statsmodels.api and logit.fit() on the dataframe I ...
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Logistics regression with polynomial features vs neural networks for classification

I am taking Andrew Ng's Coursera class on machine learning. He mentions that training a logistic regression model with polynomial features would be very expensive for certain tasks compared to ...
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Ideas to derive new column or eliminate a column for Logistic Regression problem related to binary classification

I am working on a Logistic Regression problem to identify the leads that result into a student conversion for candidature. I have 2 columns in the dataset 'Last Activity' and 'Last Notable Activity' ...
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1answer
23 views

How do we decide on the classification algorithm to use with huge training size?

I am solving a questions binary classification problem and the training size for this is huge(291 billion). The data has bloated because of using tfidfvectorizerfor ...
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How to do predict a new sms to be spam or not?

I have trained a model for spam classification - This is my code - ...
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1answer
24 views

Why do we divide the regularization term by the number of examples in regularized logistic regression?

So this is the formula for the regularized logistic regression cost function: $x^{(i)}$ - the $i$'th training example $\theta_j$ - the parameter of the $j$'th feature $m$ - the number of training ...
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9 views

Do pseudo r squared metrics make sense for classifiers that aren't logistic regression?

I'm working with some domain scientists that are used to using logistic regression to predict a binary value. One of the ways they evaluate their logistic regression model is through the Nagelkerke $...
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2answers
53 views

Testable hypotheses construction; minimum predictive strength vs. significance

Is this null hypothesis TESTABLE? Research Question: "Can a predictive model utilizing logistic regression be built which predicts at least one customer will churn in 90 days, and this individual ...
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1answer
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Can a Logsitic Regression model continue making predictions after removing predictions from the data set?

I have a logistic regression model that predicts churn (0 vs. 1). I was asked to use the model to predict on a historical group of non-churners, remove anyone who was marked as a churner, and then ...
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What type of regression to use when modeling the relationship between a continuous variable and a discrete?

I'd like to create a regression model to find the marginal effect between usage (a rate from 0 - 100%) and tap-distance (1,2,3,4...). I'm working to find how a change in tap-distance to a feature will ...
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1answer
23 views

How to find what events cluster together?

I have a data set that looks at a five-year timespan of peoples' lives and indicates if specific events have occurred (Divorce, Birth of Child, Health Shock, etc.). ...
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Significance of AUC score

My model (Logistic Regression) has AUC score of 0.8 Am I right in stating that the probability of the model ranking a random positive sample higher than a random negative sample is 0.8? Also, how ...
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1answer
51 views

Depending samples in ad ranking and click rate prediction

I am struggling with the following problem: Suppose we fit a machine learning model to model advertisers click rates. I used a Logistic Regression approach using a one-hot/dummy encoding. We have ...
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2answers
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Regression Algorithms in Production

I am interested in predicting if a doctor would prescribe a specific drug and have chosen Logistic Regression as a starting point. I have a few questions: Is feature selection the first step to take ...
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How to use logistic regression with decay

I have a dataset with binary outcomes. I use Logistic regression for making the prediction. example of my data : ...
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Is there any way to use (update) a pre-trained logistic regression model for data with new set of columns?

I am building an insurance recommendation engine. I have used some variables, like demographics, and built the model. Now I have claims data. Is there a way to include the new data without restarting ...
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Measuring the similarity between a numeric data matrix and one or more categorical variables?

Given a numeric data matrix $A$ of size $n \times p$, which each row represents an observation along $p$ variables, and a second categorical data matrix $M$ of size $n \times z$, where each row ...
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AUC ROC Threshold Setting in heavy imbalance

I am doing binary logistic regression on a dataset with very heavy class imbalance. Class 1 is only 1% of data. When I train logistic regressor without class weights I get ROC AUC Score of 0.6269. ...
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Huge cost not converging well with TensorFlow logistic regression

I try to use Logistic Regression for a dataset which contains 15 numeric features and 4238 rows of examples. The calculated cost started at 415.91, and converged when the cost was reduced to 220.119 ...
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Strategy for unrecorded predictors

I'm trying to create a logistic regression model for predicting future admissions based on historic clinical/utilization/demographic information. Although I have three years history available, for ...
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Logistic Regression performing better than SVM with a Gaussian kernel performing better than a linear SVM

I am very new to machine learning. I am working with a data set, and my algorithm for logistic regression (with lasso regularization) is performing fairly well (~0.8 AUC), my SVM with a Gaussian ...
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15 views

Looping over multiple dataframes

I have 3 sets of data that I would like to run a logisitcs regression on and then add the results to the corresponding dataframes. i.e. X1 & y1 predicted results added to df1 on so on. I've tried ...
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56 views

When to use Random Forest

I understand Random Forest models can be used both for classification and regression situations. Is there a more specific criteria to determine where a random forest model would perform better than ...
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32 views

How to Compute Logistic Regression Cost Function?

I'm using MATLAB to calculate the Logistic Regression Cost Function, and I don't get the expected output when I test my code. My steps all seem to be logically correct. Can anyone please help me fix ...
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How to check performance of a model on a test set?

I have transformed my training set (predictor variables) using step_YeoJohnson for satisfying the assumptions of model. But now how do I run my model on test set which is not transformed and has ...
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1answer
56 views

Automating Logistic Regression

I have $3$ datasets I'd like to run a logistic regression on split into $X_1, y_1$ $X_2, y_2$ $X_3, y_3$ How can I run a loop so that I can run an automated ...
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Manually changing coefficients of a model

Is there a way to manually change parameters of a model in Orange 3? I have a dataset and trained a logistic regression model on it. Now, I can alter the coefficients (beta values) of the logistic ...
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Build model to get daily probability of meeting a certain end of period goal

I was hoping for some consultation and direction with how to go about the following: To give context, I work for an agency that manages advertisements on social media for general motors - ...
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3answers
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Logistic Regression doesn't predict for the entire test set

I am working through Kaggle's Titanic competition. I am mostly done with my model but the problem is that the logistic regression model does not predict for all of 418 rows in the test set but ...
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1answer
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In the context of image binary classification, is it necessary to divide dataset into true positive, false positive, true negative, false negative?

I am working through this course. It seems that the professor is not dividing the dataset into true positive, false positive, true negative, and false negative. In the context of image binary ...
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2answers
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What is the purpose of Logit function? At what stage of model building process this logit function is used?

We have two prominent functions (or we can say equations) in logistic regression algorithm: 1. Logistic regression function. 2. Logit function. I would like to know: a. Which of these equation(s) is/...
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84 views

Why does the logistic regression cost function need to be the negative of log?

I am going through this course. The professor is talking about the logistic regression cost function: $$ P(y|x) = \hat y^y (1- \hat y)^{(1-y)} $$ Taking log on both sides provides: $$ \begin{align} ...
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Data Structure For Multilevel Analysis

I am little confused about how to structure my specific data for multilevel analysis. I have 10 categories and each category has some items in them. The dataset is available for 117 weeks. There is ...