Questions tagged [linear-regression]

For questions related to the theory or application of linear regression.

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29 views

Linear output layer back propagation

So I'm stack to something that it's probably very easy but I can't get my head around it. I'm building a Neural Network that will consist of many layers with non-linear activation functions (probably ...
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1answer
59 views

Is there a connection between the bias term in a linear regression model and the bias that can lead to under-fitting?

Here is a linear regression model $$y = mx + b,$$ where $b$ is known as $y$-intercept, but also known as the bias [1], $m$ is the slope, and $x$ is the feature vector. As I understood, in machine ...
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1answer
36 views

How parameter adjustment works in Gradient Descent?

I am trying to comprehend how the Gradient Descent works. I understand we have a cost function which is defined in terms of the following parameters, $J(𝑤_{1},𝑤_{2},.... , w_{n}, b)$ the derivative ...
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0answers
22 views

Is there a UCB type algorithm for linear stochastic bandit with lasso regression?

Why is there no upper confidence bound algorithm for linear stochastic bandits that uses lasso regression in the case that the regression parameters are sparse in the features? In particular, I don't ...
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0answers
32 views

linear regression using gradient decent

I am novice in machine learning and try to implement Linear regression algorithm using Gradient Descent. I am using Google Co-...
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1answer
32 views

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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1answer
19 views

Effect of adding an Independent Variable in Multiple Linear Regression

I am new in machine learning and learning linear regression concept. Please help with answers to below queries. I want to understand effect on existing independent variable(X1) if I add a new ...
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1answer
39 views

What ML algorithm should I use that suits this data?

What if I have some data, let's say I'm trying to answer if education level and IQ affect earnings, and I want to analyze this data and put in a regression model to predict earnings based on the IQ ...
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0answers
45 views

Predict probability of user making a conversion

My dear friends, In the past couple of years I read a lot about AI with JS and some libraries like TensorFlow. I have great interest in the subject but never used it on a serious project. However, ...
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1answer
42 views

Do correlations matter when building neural networks?

I am new to working with neural networks. However, I have built some linear regression models in the past. My question is, is it worth looking for features with a correlation to my target variable as ...
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1answer
36 views

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
64 views

If features are always positives, why do we use RELU activation functions?

When does it happen that a layer (either first or hidden) outputs negative values in order to justify the use of RELU? As far as I know, features are never negative or converted to negative in any ...
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0answers
27 views

3d representation of a regression with two independent variables one of them is categorical and another is continuous

I have hopefully a fundamental question of Do I understand things right. (Thank you in advance and sorry for my English which might be not so good) 1-Preambula 1: I know that if we have 2 independent ...
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1answer
58 views

Is there any way to apply linear transformations on a vector other than matrix multiplication?

I am trying to optimize the cost function calculation in regression analysis using a non-matrix multiplication based approach. More specifically, I have a point $x = (1, 1, 2, 3)$, to which I want to ...
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0answers
86 views

How to implement Mean square error loss function in mini batch GD

I have a vectorized implementation of the neural network in c++. I successfully solve the classification problems of Fashion MNIST and CIFAR. Now I am modifying my code to do the Linear regression. I ...
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1answer
55 views

Do I need to denormalise results in linear regression?

I have learned so far how to linear regression with one or multiple features. So far, so good, everything seems to work fine, at least for my first simple examples. However, I now need to normalise ...
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0answers
27 views

What is the difference between an generalised estimating equation and a recurrent neural network?

What is the difference between a generalised estimating equation (GEE) model and a recurrent neural network (RNN) model, in terms of what these two models are doing? Apart from the differences in the ...
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20 views

Computing latent representation for multi-domain regression/classification

Suppose I have a dataset with (X, Y) training samples where X is a 1 dimension, and Y is also 1 dimension. Example: if this is a housing price dataset, X would be an area in square feet, and Y would ...
2
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1answer
77 views

What is the difference between linear and non-linear regression?

In machine learning, I understand that linear regression assumes that parameters or weights in equation should be linear. For Example: $$y = w_1x_1 + w_2x_2$$ is a linear equation where $x_1$ and $...
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2answers
185 views

How do we choose the activation function for each hidden node? [duplicate]

I am new to neural networks. I would like to use them as a fitting or forecasting method. A simple NN model that does not contain hidden layers, that is, the input nodes are directly connected to the ...
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2answers
39 views

Is it still called linear separation with a layer of more than 1 neuron

A single neuron will be able to do linear separation. For example, XOR simulator network: ...
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1answer
30 views

Solution to classify product names

I have a bunch of training data for classifying product names, around 30,000 samples. The task is to classify these product names into types of product, around 100 classes (single words). For example:...
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1answer
58 views

TensorFlow estimator DNNClassifier fails to fit simple data

The ready-to-use DNNClassifier in tf.estimator seems not able to fit these data: ...
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4answers
124 views

How is regression machine learning?

In regression, in order to minimize an error function, a functional form of hypothesis $h$ must be decided upon, and it must be assumed (as far as I'm concerned) that $f$, the true mapping of instance ...
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1answer
43 views

Calculating Parameter value Using Gradient Descent for Linear Regression Model

Consider the following data with one input (x) and one output (y): (x=1, y=2) (x=2, y=1) (x=3, y=2) Apply linear regression on this data, using the hypothesis $h_Θ(x) = Θ_0 + Θ_1 x$, where $...
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2answers
241 views

Is there a machine learning algorithm to find similar sales patterns?

I have a dataset as follows (and the table extends to include an extra 146 columns for companies 4-149) Is there an algorithm I could use effectively to find similar patterns in sales from the other ...
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1answer
53 views

Auto-regression - Reduce error in prediction

I am trying to develop a time series model using autoregression. The data set is like as follows ...
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0answers
129 views

Actor-critic algorithm using gaussian Radial Basis Function, Local Linear Regression and shallow Neural Network

I'm attempting to implement the actor-critic algorithm on Matlab using Radial Basis Function, Local Linear Regression, and shallow Neural Network for inverted pendulum system. the state space and the ...
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0answers
40 views

Can we compare MAE MSE results with categorical_crossentropy?

can i compare MAE and MSE loss results of a regression CNN with categorical_crossentropy loss of a classification CNN if they both have similar tasks? is yes how to?
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0answers
27 views

What Model Used for Forecasting Sales with Dynamic Holiday

I'm working on a project where I need to forecast sales data where I have history of 1 year (2017) daily data. I am new on Artificial Intelligence topic and after searching for a while, I think ARIMA ...
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2answers
110 views

How to make machine learning model that reports ambiguity of the input?

Suppose I want to build a neural network regression model that takes one input and return one output. Here's the training data: ...
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1answer
58 views

Will LMS always be convex function? If yes, then why do we change it for neural networks?

In LMS(least mean square) since, we use a quadratic error function, and quadratic functions are generally parabola in (some convex like shape). I wonder whether that is the reason why we use least ...
3
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2answers
179 views

What makes a machine learning algorithm a low variance one or a high variance one?

Some examples of low-variance machine learning algorithms include linear regression, linear discriminant analysis, and logistic regression. Examples of high-variance machine learning algorithms ...
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1answer
204 views

Does the correlation between inputs affect the model performance?

I'm currently working on a regression problem and I have 10 inputs/attributes. What should I do if there are correlations between different features of the input data? Does the correlation between ...
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1answer
35 views

Is there a relationship between the response and predictors?

I have been reading introduction to statistical learning, and I was going through multiple linear regression. This is the topic that I'm reading: As I was reading further, I encountered an equation ...
2
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1answer
76 views

Understanding the math behind using maximum likelihood for linear regression

I understand both terms, linear regression and maximum likelihood, but, when it comes to the math, I am totally lost. So I am reading this article The Principle of Maximum Likelihood (by Suriyadeepan ...
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3answers
1k views

What is the difference between hypothesis space and representational capacity?

I am reading Goodfellow et al Deeplearning Book. I found it difficult to understand the difference between the definition of the hypothesis space and representation capacity of a model. In Chapter 5,...
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2answers
2k views

Matrix Dimension for Linear regression coefficients

While reading about least squares implementation for machine learning I came across this passage in the following two photos: Perhaps I’m misinterpreting the meaning of beta but if X^T has dimension ...
5
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1answer
266 views

Regression on extreme values

I have a data set that looks like this: I would like to estimate a relationship between x-values and the corresponding 5% extreme y-values, something that might look like that : Do you have an idea ...
4
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1answer
874 views

How do you handle multiple categorical values in a single column for wide_deep model in tensorflow? [closed]

To start, let me just say that I am very new to tensorflow and Machine Learning in general. But, as part of my learning project I am trying to adapt the tensorflow wide and deep model to generate ...
6
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0answers
340 views

Can we use the recursive least squares as a learning algorithm to an ADALINE?

I'm new to neural network, I study electrical engineering, and I just started working with ADALINEs. I use Matlab, and in their Documentation they cite : However, here the LMS (least mean squares) ...
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2answers
534 views

How is direction of weight change determined by Gradient Descent algorithm

The result of gradient descent algorithm is a vector. So how does this algorithm decide the direction for weight change? We Give hyperparameters for step size. But how is the vector direction for ...
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1answer
80 views

multi vs one prediction using Regression

I was trying to build a prediction system where I have the input data arranged in multiple columns. The input data would be of the type where I have weather, service type (bronze, silver, gold), size(...
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2answers
63 views

In the multi-linear regression, how is the value of weight $b_2$ calculated?

In multivariate linear regression (linear regression with more than one variable) the model is $yi = b_0 + b_1x_{1i} + b_2x_{2i} + ...$ , and so on. But how is the $b_n$ value calculated iteratively? ...
3
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3answers
223 views

Understanding a few terms in Andrew Ng's definition of the cost function for linear regression

I have completed week 1 of Andrew Ng's course. I understand that the cost function for linear regression is defined as $J (\theta_0, \theta_1) = 1/2m*\sum (h(x)-y)^2$ and the $h$ is defined as $h(x) = ...
4
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2answers
870 views

What to do if CNN cannot overfit a training set on adding dropout?

I have been trying to use CNN for a regression problem. I followed the standard recommendation of disabling dropout and overfitting a small training set prior to trying for generalization. With a 10 ...
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0answers
23 views

Can number of Leads be predicted based on previous months

I have a sample set of data about Leads that gets generated every day. Leads are nothing but a user expressing request to be our partner or not. Sample data set is as shown below ...
3
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1answer
115 views

Predict value from image set

I have a large dataset of skin images, each one associated with a hydration value (percentage). Now I'm looking into predicting the hydration value from an image. My thinking: train a CNN on the ...
2
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1answer
44 views

How to choose evaluation functions for features, when network effects are in place (multi-agent systems)?

So, I have this huge amount of data, which has 7 vector features (float from 0 to 1). I am trying to build a kind of recommendation system, with a twist (it uses agents and negotiations and narratives;...
2
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0answers
71 views

What are suitable predictive analytics models for data from multiple sensors?

I am a newbie in the field of AI/ML. I am trying to implement predictive analytics model on the data generated and collected every minute from a device with sensors. I have two questions: What are ...