Questions tagged [logistic-regression]

For questions related to Logistic regression in the context of machine learning and AI. Logistic regression is a statistical classification model used for making categorical predictions.

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Does it make sense for a logistic regression model to perform better than a neural network on the Iris data set?

Per a review post, a simple Logistic Regression model on the Iris data set gets about 97% test accuracy on iris dataset whereas a neural network gets just 94%. The neural network model used in Keras ...
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Is it possible to compute the logical AND and OR with logistic regression?

It's easy to build a perceptron that can compute the logical AND and OR functions of its binary inputs. Logistic regression could be used as a binary classifier. $$z^{(i)} = w^T x^{(i)} + b$$ $$\hat{y}...
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Is the main difference between the logistic regression and the perceptron the activation function they use?

I went through a Stats StackExchange's post about the difference between logistic regression and perceptron, which is too long to get the key point. I'd like to consider the question in terms of the ...
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Derivation of an probability expansion used in Word2Vec classifier model

We are using the following notations, for this question, to calculate the probability values \begin{array}{|c|c|} \hline \text{$w$} & \text{target word embedding vector} \\ \hline \text{$c$} &...
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1answer
79 views

How to get more accuracy of the logistic regression model?

I am working on a Baby Crying Detection model using logistic regression. Out of $581$ audios, $222$ are of a baby crying. Each audio is of $5$ seconds. what I have done is convert each audio into ...
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Architecture for Logistic Regression with Arbitrary Number of Options

Suppose I want to design a neural network to choose one of several mutually exclusive options. This may normally be done via logistic regression, where the input of the network would be a [batch x ...
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22 views

How do we interpret the images of weights in logistic regression

The following images are a) The weights of a logistic regression model trained on MNIST. b) The sign of the weights of a logistic regression How do these images represent the weights? Would be ...
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1answer
80 views

What is meant by "the number of examples is reduced", and why is this the case?

I am currently studying the paper Learning and Evaluating Classifiers under Sample Selection Bias by Bianca Zadrozny. In section 3.2. Logistic Regression, the author says the following: 3.2. Logistic ...
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57 views

Does the image is logistic regression or SVM, and why? [closed]

Does the image is logistic regression or SVM, and why?
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70 views

How to frame this problem using RL?

How should this problem be framed in the domain of RL for preventing users from exceeding their bank account balance and being overdrawn? For example, a user has 1000 in an account, and proceeds to ...
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Neural network algorithm implementation for Iris dataset

I want to use Neural network algorithm over famous Iris dataset. Iris dataset attributes names sepal length in cm sepal width in cm petal length in cm petal width in cm Sample dataset: ...
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1answer
50 views

Back propagation approach to logistic regression: why is cost diverging but accuracy increasing?

Background I have tried to fit a logistic regression model - written using a forward / back propagation approach (as part of Andrew Ng's deep learning course) - to a very non-linear data set (see ...
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Hyper-plane in logistic regression vs linear regression for same number of features

Geometric interpretation of Logistic Regression and Linear regression is considered here. I was going through Logistic regression and Linear regression. In the optimization equation of both following ...
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Given the same features, do logistic regression and neural networks produce the same output?

I have a binary classification problem. I have variables (features) var1, var2, var3, ..., var14. Using these variables (aka features) in a logistic regression, I get their weights. If I use the same ...
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1answer
57 views

How could logistic loss be used as loss function for an ANN?

Normally, in practice, people use those loss functions with minima, e.g. $L_1$ mean absolute loss, $L_2$ mean squared error, etc. All those come with a minimum to optimize to. However, there's ...
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1answer
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Why is the hypothesis function $h_{\theta}(x)$ equivalent to $E[y | x; \theta]$ in generalised linear models?

Reading through the CS229 lecture notes on generalised linear models, I came across the idea that a linear regression problem can be modelled as a Gaussian distribution, which is a form of the ...
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1answer
61 views

How does the weight update formula for logistic regression work?

I am trying to use Logistic Regression to make a spam filter, but I am having trouble understanding the weight update part. I have processed my email dataset, and I have an attribute vector of the top ...
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36 views

Is logistic regression used for unconstrained or constrained optimisation problems?

Is logistic regression used for unconstrained or constrained optimization problems, and why?
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1answer
51 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 (Total Cholesterol, Systolic Blood Pressure, Diastolic Blood Pressure, and Cigraeette count) to do a Binominal Classification (find stroke likelihood) ...
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2k views

Why not use the MSE instead of the current logistic regression?

When watching the machine learning course on Coursera by Andrew Ng, in the logistic regression week, the cost function was a bit more complex than the one for linear regression, but definitely not ...
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58 views

Checking Whether Given Logistic Regression Classifier classifies data

A bank wants to decide whether a customer can be given a loan, based on two features related to (i) the monthly salary of the customer, and (ii) his/her account balance. For simplicity, we model ...
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65 views

How to define cost function for custom nonlinear functions?

For logistic regression, the Cost function is defined as: \begin{equation} Cost(h_{\theta}(x)-y) = -ylog(h_{\theta}(x))-(1-y)log(1-h_{\theta}(x)) \end{equation} I now have a nonlinear function \begin{...
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What is the right formula for weight update rule in Logistic Regression using stochastic gradient descent

Apologies for the lengthy title. My question is about the weight update rule for logistic regression using stochastic gradient descent. I have just started experimenting on Logistic Regression. I ...
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35 views

How can the bias and the coefficient be calculated in logistic regression?

How can the bias, $b_0$, and the coefficient for the single input value, $b_1$, be calculated in logistic regression? I am trying to calculate these coefficients with pure Python. Can anybody ...
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1answer
55 views

How many parameter would there be in a logistic regression model used to classify reviews into "good" or "bad"?

Suppose we want to classify a review as good ($1$) or bad ($0$). We have a training data set of $10,000$ reviews. Also, suppose we have a vocabulary of $100,000$ words $w_1, \dots, w_{100,000}$. So ...
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Is logistic regression more free from the conditional independence assumption than naive Bayes?

To my understanding, logistic regression is an extension of naive Bayes. Suppose $X = \{x_1, x_2, \dots, x_N \}$ and $Y = \{0, 1\}$, each $x_i$ is i.i.d and $P(x_i \mid Y=y_k) \sim \mathcal{N}(\mu, \...