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.
Questions tagged [logistic-regression]
34 questions
5
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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 $P(x_i \mid Y=y_k) \sim \mathcal{N}(\mu, \sigma^2)$ is a Gaussian distribution.
In order to…
imflash217
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4
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What are the differences between softmax regression and logistic regression (other than when the number of classes is 2)?
I read about softmax from this article. Apparently, these 2 are similar, except that the probability of all classes in softmax adds to 1. According to their last paragraph for number of classes = 2, softmax reduces to LR. What I want to know is…
user9947
3
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1 answer
Why does my regression-NN completely fail to predict some points?
I would like to train a NN in order to approximate an unknown function $y = f(x_1,x_2)$. I have a lot of measurements $y = [y_1,\dots,y_K]$ (with K that could be in the range of 10-100 thousands) which come out from either a simulation or a…
MttRch
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3
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1 answer
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 regression
In logistic regression, we use…
The Pointer
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3
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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 that hard.
But it got me thinking, why not use the…
user17894
3
votes
0 answers
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 came across two weight update expressions and did not…
GYSHIDO
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2
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2 answers
Are there any toy classification problems that can't be solved with logistic regression, but can be solved with a NN with exactly one hidden node?
Basically, I'm wondering if there are any small and simple problems that are:
complex enough to be unsolvable with a standard neural network without any hidden layer (ie. input -> output)
simple enough to be solvable with a standard neural network…
J Doug
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2
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2 answers
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 is
model = tf.keras.Sequential([
…
JJJohn
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2
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1 answer
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 numbers. and those numbers go into a .csv file. so…
Muhammad Waqar Anwar
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2
votes
1 answer
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 exponential family. The notes state that $h_{\theta}(x)$…
calveeen
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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 n words that are most likely to be contained…
kostas
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0 answers
Is logistic regression used for unconstrained or constrained optimisation problems?
Is logistic regression used for unconstrained or constrained optimization problems, and why?
Miko
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2
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1 answer
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) using Logistic Regression Algorithm.
I made sure…
GYSHIDO
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2
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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{equation}
…
yfliuuu
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1
vote
1 answer
Are these steps to get a final linear regression model correct?
I am new to machine learning. I know Logistic Regression (LR) is a supervised learning technique. Therefore, we need training data to train the model.
I tried to understand the basic steps to get the final RL model.
According to my understanding,…
Ind
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