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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Are linear models better when dealing with too many features? If so, why?
I had to build a classification model in order to predict which what would be the user rating by using his/her review. (I was dealing with this dataset: Trip Advisor Hotel Reviews)
After some ...
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0answers
23 views
What should be the target variable shape? should it be (n,1) or (n,)?
I tried logistic regression on the titanic dataset but used 2 different code:
1st code:
...
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1answer
26 views
Where shall I use odds logarithm and when shall I use sigmoid in logistic regression?
I have been interested in DS and ML recently and logistic regression was on of the first algorithms I learned. In my first course it was said that ln(p/(1-p) was used for the logistic regression. But ...
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2answers
29 views
MLE & Gradient Descent in Logistic Regression
In Logistic Regression, MLE is used to develop a mathematical function to estimate the model parameters, optimization techniques like Gradient Descent are used to solve this function.
Can somebody ...
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1answer
32 views
Improving precision and recall for imbalanced large data set
I have a data set of 1 million points and 30 features. The output variable has multiple classes (1 to $n$) but the problem I'm interested in is only concerned whether the output belongs to class 1 or ...
2
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1answer
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Logistic Regression - Odds & log of odds
ln(p1−p)=β0+β1X
The equation of line in the above equation denotes that the log of odds is linearly related to the predictor variables.
Why is log of odds linearly related to the predictor variables, ...
0
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1answer
24 views
Logistic regression - Odds ratio vs Probability
In Logistic regression, the final values we achieve are associated with Probability. Then why do we need Logit/Log of odds? We can directly use probability.
Is Logit used to get the equation of a best ...
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2answers
43 views
Relation between MLE (Maximum Likelihood Estimation) & Gradient Descent
What are the similarities & dissimilarities between MLE (used to find the best parameters in logistic regression) & Gradient Descent?
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1answer
15 views
Logistic Regression mapping formula
Sigmoid function predicts the probability value which is between 0 & 1. What is the formula in logistic regression that maps the predicted probabilities to either 1 or 0?
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33 views
Mapping values in Logistic Regression
When mapping probabilities obtained in logistic regression to 0s & 1s using the sigmoid function, we use a threshold value of 0.5. If the predicted probability lies above 0.5, then it gets mapped ...
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1answer
12 views
How to customize logistic regression for this case?
I have a binary classification problem, with a dataset comprising of several features. When I train LogisticRegression on it, I get large number of false positives ...
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0answers
11 views
How to use principal components in R to apply a multinomial logistic regression? [closed]
Context: I have an original dataset of +- 20k rows (samples) and 253 features (deletions, insertions and substitutions). These columns/dimensions/features are called SNVs (single nucleotide variants ...
3
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1answer
357 views
In machine learning, what is the difference between a probabilistic approach and a geometric approach?
For example:
The probabilistic approach of logistic regression involves the MLE (maximum likelihood estimation) maximizing the likelihood function, or in other words, finding the best parameters for ...
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Logistic regression forms
If we have dataset $D=(X,\Theta)$, where $X={x^{(i)},y^{(i)}}, i=1, \cdots, n$ and $\Theta$ representing our parameters of the model that we want to learn, a simple form can be the following:
$$
p\...
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1answer
34 views
Gradient descent implementation of logistic regression
Objective
Seeking for help, advise why the gradient descent implementation does not work below.
Background
Working on the task below to implement the logistic regression.
Gradient descent
Derived the ...
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0answers
15 views
Complete separation of logistic regression
I came over problem that might occur in logistic regression. In case dataset is too small to observe events with low probabilities. For example, in the following table we have response set to 0 if $X\...
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1answer
22 views
Finding logistic loss/negative log likelihood - binary logistic regression classification
I am new to ML and data science and am struggling with a simple problem. In my problem, I am given a series of datapoints $X_i$ where $X_i = (x_{i1}, x_{i2})$ with each data point having a label $y_i$ ...
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0answers
20 views
Linear combination of features reverses importance of all features
I am trying with a logistic model with 2 features independently or with linear combination, but in the linear combination, combining these features would reverse importance through significance levels ...
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0answers
18 views
Why is PyTorch's Dataloader is not inerrable?
I am working on MNIST dataset for an assignment and it seems to be I am stuck at some point for long. I have wrote my code for LogisticRegression and when I try to train the model it is not working as ...
0
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1answer
19 views
Kernel dies or proses stuck when making LR prediction on dataframe using apply
I'm trying to making predictions with a simple model.
model=LogisticRegression()
model.fit(X_train,y_train)
After fitting, i try to make predictions. A sample ...
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1answer
53 views
Logistic Regression - Probabilistic intuition vs Geometric intuition
The Probabilistic approach of logistic regression involves the MLE (Maximum Likelihood Estimation) maximizing the likelihood function, or in other words, finding the best parameters for the best fit ...
3
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4answers
189 views
Why are deep learning models unstable compare to machine learning models?
I would like to understand why deep learning models are so unstable. Suppose I use the same dataset to train a machine learning model multiple times (for example logistic regression) and a deep ...
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1answer
29 views
Cost function - Log Loss query
What is the purpose of using "log" in the logistic regression cost function "log loss"?
0
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1answer
60 views
How python LogisticRegression() binary classification works for more than 2 independent variables
I created bag of words (in which number of columns were around 15000 i.e greater than 1) and corresponding output data (0 or 1). After that I used LogisticRegression() (I didn't passed any parameters) ...
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0answers
33 views
Can any data be learned using polynomial logistic regression?
We know that a Taylor polynomial can approximate any smooth function. In binary logistic regression we're trying to fit a decision boundary to our data. But this decision boundary is not necessarily a ...
0
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1answer
20 views
Do I have to remove features with pairwise correlation even if I am doing a regularized logistic regression?
Normally we would remove features that have high pairwise correlation with another feature before performing regression. But is this step necessary if I am applying L2 regularized logistic regression (...
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0answers
13 views
Low-value coefficients in logistic regression
I prepared a logistic regression model. I then checked the coefficients/weights of the features, some of which were close to 0, which means these features do not contribute much to our predictive ...
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2answers
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log(odds) to p formulation
$$Log(Odds) = log({p \over (1-p)}) $$
$${p \over (1-p)} = e^{b+b_1x_1+....}$$
I understand up to here, however how does this:
$$p = (1-p) e^{b+b_1x_1+...}$$
become:
$$ p = {1 \over {1+e^{-(b+b_1x_1+......
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1answer
27 views
Normalization factor in logistic regression cross entropy
Given that probability of a matrix of features $X$ along with weights $w$ is computed:
...
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1answer
24 views
When should we use jaccard score?
I am a newbie in Machine Learning, I trained a binary classifier for bank loan prediction through Logistic Regression.
I measured the accuracy of it with two methods: accuracy score and jaccard index.
...
2
votes
1answer
20 views
What is the best Classification Method alternative to Nominal Logistic Regression, if your Response and all Predictor variables are Categorical?
Hy, I need help in choosing the best classification method. My response variable is nominal with "4" categories and five predictor variables, two of them are nominal and three are binary. ...
0
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1answer
22 views
creating a logistic regression model with coefficients
I am trying to understand the details of the logistic regression models and now I was wondering how the model can be created if you have the coefficients and intercepts.
So I created a logistic ...
0
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1answer
25 views
Understanding intution behind sigmoid curve in the context of back propagation
I was trying to understand significance of S-shape of sigmoid / logistic function.
The slope/derivative of sigmoid approaches zero for very large and very small input values. That is $σ'(z) ≈ 0$ for $...
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0answers
12 views
Update function for NN with logistic and sofmax
Can anyone help me confirm my work or find resources on how to come up with the update function for each layer in the Neural Network for multi-class classification problem i.e I am using logistic as ...
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1answer
25 views
One predictor variable and 3 response variable (categorical and continuous) [closed]
If I have predictor variables which are a mixture of continuous and categorical, and a response variable that is continuous.
What approach should I apply? Linear regression, logistic regression or k ...
3
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2answers
3k views
My data is highly overlapping, but when I apply logistic regression, it is giving an impressive accuracy of 79%. Why?
Logistic regression is supposed to work well only on data that is linearly separable. As we can see in the pair plot, the data points heavily overlap. The logistic regression model is in fact showing ...
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3answers
66 views
How to get the correct confusion matrix in imbalance class dataset?
I have created two simulated random dataset of 3 classes. Only difference between the dataset is that frequency of the classes.
...
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1answer
22 views
Comparison of classifier confusion matrices
I tried implementing Logistic regression, Linear Discriminant Analysis and KNN for the smarket dataset provided in "An Introduction to Statistical Learning" in python.
Logistic Regression ...
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0answers
254 views
Binary classification with imbalanced dataset, about lightgbm output probability distribution
I trained a binary classifier for an imbalanced dataset. I did two experiments:
lightgbm classifier, boosting_type='gbdt', objective='cross_entropy', SMOTE upsample
After training the lgbm model, I ...
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0answers
8 views
How to select features for a multivariable analysis with a small sample?
I've applied boruta method (RF) to select variables and applied them on a multivariate logistic regression, but the reviewer of my paper questioned the results once this method is "data-hungry&...
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0answers
25 views
Time complexity of scikit-learn implementations of RandomForestClassifier and LogisticRegression
Is there a documented source of the time complexities taken by sklearn implementations of supervised algorithms - specifically of RandomForestClassifier and ...
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0answers
23 views
What is correct equation for LR decision boundary?
I read that the equation perceptron decision boundary is given as follows:$$w^Tx-w_0=0$$
This can be proven as follows:
Assuming $w$ is a unit vector (as we can multiply above equation with a ...
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0answers
6 views
How to predict categorial variable with another variable which is quantitative if present and qualitative if missing?
Here is my 2 step biological problem :
Step 1 :
I track single cells through time in order to detect parameter A
At the end of this step, whether a single cell presented the parameter A and I record ...
0
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0answers
171 views
Correlation between continuous variables and multi class categorical variables in python
I was trying to figure out a way of finding a correlation between continuous variables and a non-binary target categorical label. The only thing I though of is by fitting the labels into Multinomial ...
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0answers
36 views
Permutation Importance is 0 with high accuracy
I am using sklearn's permutation_importance for feature selection, and all my features return score decreases of 0, even though my model accuracy is 0.96.
I have ...
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0answers
67 views
Logistic Regression Model returning same output for all inputs
I made a simple linear regression model with a simple .csv dataset that had 2 categories.
For reference, the dataset looks like a larger version of this
Basically, it is a hobby classification ...
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0answers
21 views
Mutiple binary classification for for best propensity to buy one of the product
Problem:- I have 5 products for sell and I can pitch only one product in a month to one customer.so I wants to know which product customer can buy.
Proposed solution:-
I build 5 binary logistic models ...
2
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1answer
55 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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0answers
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Sudden jumps in accuracy with logistic regression and bag of words : "glm.fit: algorithm did not converge"
I work on a bag of words, on the Toxic Comments Classifications challenge. The challenge is closed but the dataset is very nice to learn.
I use R, tf-idf, tm, and logistic regression.
I have a strange ...
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1answer
23 views
How to choose the right predictors for a classfication model?
I am working on a classification problem. I have two models:
Logistic regression model
Random Forest model
For the first model, if I choose the only predictors with p-values<0.05 I will reduce ...
