Questions tagged [linear-regression]

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

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2answers
34 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
22 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
15 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
38 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
103 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
25 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
114 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
33 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
48 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
29 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
20 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
36 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
34 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 ...
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1answer
73 views

how to predict the stock of food items using ai

Actually, I want to make an AI model which tells the seller about the maintaining stock of food items as a parameter of time and eventually learns by itself with the customer buying data (problem - ...
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1answer
96 views

Why KNN, Decision Trees, etc have a high variance?

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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3answers
137 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
21 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 ...
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1answer
54 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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1answer
433 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
897 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
250 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 ...
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1answer
671 views

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

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 ...
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0answers
241 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
230 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
68 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
54 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? ...
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4answers
140 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) = ...
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2answers
598 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
19 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 ...
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1answer
65 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
31 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
64 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 ...
5
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2answers
839 views

Deep Learning – Classification or Regression Approach?

I am fairly new to deep learning in general and I am currently facing a problem I want to solve using neural networks and I am unsure if it is a classification or regression problem. I am aware that ...
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2answers
172 views

Is Deep Learning the repeated application of Linear Regression?

Looking for an explanation of the linear regression estimation method in deep learning.
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5answers
1k views

Linear regression: why is distance *squared* used as an error metric?

Usually when performing linear regression predictions and gradient descent, the measure of the level of error for a particular line will be measured by the sum of the squared-distance values. Why ...
3
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1answer
131 views

With gradient descent w/MSE on a regression, must/should every Epoch use the exact same training samples?

Let's say I've got a training sample set of 1 million records, which I pull batches of 100 from to train a basic regression model using gradient descent and MSE as a loss function. Assume test and ...
3
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0answers
63 views

How exactly does a validation data-set work work in machine learning? [closed]

With typical machine learning you would usually use a training data-set to create a model of some kind, and a testing data-set to then test the newly created model. For something like linear ...