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Questions tagged [machine-learning]

Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.

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42 views

Which algorithm does Decision Tree classifier in sklearn implement?

Which algorithm does Decision Tree classifier in sklearn Library implement? Is it GUIDE? There are a total of 6 techniques available according to my knowledge, according to this paper
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0answers
33 views

How to evaluate similarity metric using classifiers and clustering techniques?

I was going through this paper which proposes a new similarity metric. The evaluation is carried out using various classification and clustering techniques. I was confused about how a similarity ...
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1answer
18 views

Binary Classification of Numeric Sequences with Keras and LSTMs [duplicate]

I'm attempting to use a sequence of numbers (of fixed length) in order to predict a binary output (either 1 or 0) using Keras and a recurrent neural network. Each training example/sequence has 10 ...
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2answers
73 views

About comparing machine learning algorithms [on hold]

I'm working on comparing 2 algorithms with an experimental protocol that produce 100 folds for each one. As a result, I found that my algorithm got (49.29 $\pm$ 1.69) and the baseline got (50.40 $\pm$...
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29 views

Regression of arbitrary equations

I am looking for something like a linear regression but instead of the equation being linear it can be completely arbitrary. e.g. For the equation of gravity, we already know $F = \frac{G m_1 m_2 }{ ...
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0answers
17 views

Choosing threshold for Imbalance Data

I have a question of choosing the threshold of an imbalanced data. In my data set, there are 90% of the data belongs to class 0 in the dependent variable, and only 10% belongs to class 1. If I fit a ...
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0answers
18 views

Is the Markov Network (Markov Random Field) property biconditional?

As far as I know, the property of a Markov Random Field is defined as follows: Let $G = (V, E)$ be a Markov Network. Let $X, Y, C \subseteq V$. If every path from a vertex in $X$ to a vertex in $Y$ ...
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1answer
25 views

Do decision tree's perform variable selection?

I'm a bit confused how decision tree's select the variables to split. I know they splitt the data set through variable to get a more pure data set. But can it happend that some explenatory variables ...
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0answers
15 views

How does one go about evaluating a new loss function? [closed]

Suppose a new loss function similar to log loss is proposed. Now we have to check whether it'll work in practical scenarios in general and tractable. What experiments one should run get to some ...
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1answer
26 views

How to recover primal problem from its dual counterpart

I am asking this from context of optimization in machine learning. We often talk about a primal problem and how this primal problem can be solved by first converting it into a dual problem (Using ...
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1answer
16 views

Fisher's Linear Discriminant Analysis

I have a dataset with two classes and I want to apply Fisher's Linear Discriminant Analysis. To train the model, in what order do I need to compute the following:The within-class scatter,The sample ...
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1answer
11 views

What does having a stronger gradient mean intuitively when talking about various activation functions?

I was reading about the various activation functions that are available to choose from. For example: Sigmoid activation function Tanh activation function Relu activation function etc.. I came across ...
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43 views

What is VC dimension of linear classifier? How to measure it?

I came to know about Vapnik–Chervonenkis during my study on ML. All I get it to represent the power of a classifier. But I don't understand how to calculate it exactly. Given ...
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1answer
15 views

problem in finding the calculated auc and aupr values

This is the code for k-seeds clustering where features and side_effects are the excel files. I am facing problem in understanding the results. Where to find the calculated AUC and AUPR values? And ...
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0answers
24 views

Bootstrapping vs. K-Fold: Is every data point in atleast one of the test set/out of bag - atleast once?

It's easy to see that in K-Fold cross-validation, that split training examples into K parts, in such a way that 1 of the K parts is considered to be the test set, and eventually as you shift which ...
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0answers
33 views

Still Overfitting SVM with Cross-Validation and Grid Search

I am relatively new to machine learning and am trying to implement an SVM for the first time on a project, but I'm running into some overfitting-related issues. Basically, I created a function called ...
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0answers
21 views

How Gini/AUC of two features is bounded by individual features?

Consider binary classification problem and popular quality measure ROC AUC (which is almost the same as Gini coefficient G = 2*AUC - 1 ). Assume we have two features F1, F2. Question (rough version)...
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0answers
28 views

Wrong calculation of feature importance of decision tree in R [closed]

I trained decision tree both in python and R, but I think the way feature importance is calculated in R may be wrong. Following is the sample code which you can use to reproduce the problem. Let's say ...
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2answers
30 views

Can a decision tree make a decision based on two variables at one split?

I know that the random forest algorithm works by generating a set of decision trees with a subset of features. Say I was using random forest as a classification algorithm looking at someone's data ...
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0answers
27 views

Determining if time series follows a pattern

I was wondering if anyone had any idea how to solve this problem. So basically I have a dataset where some person approximately comes at some regular interval and I don't know what that interval is. ...
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0answers
17 views

Why is “consistent nearest neighbour” Non-parametric? [duplicate]

Definition of "Consistent nearest neighbour", runs our usual KNN classifier but instead of viewing k as a hyper-parameter it always sets k = ceil[log(n)]. So far, I looked-up many references and ...
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1answer
38 views

What is the trade off between having a larger validation set versus a smaller one?

Suppose I am comparing several models, e,g, $\{ax\}$, $\{ax+b\}$ and $\{ax^2 + bx + c\}$, $\{ax^3\}$ on data set $\mathcal{D} = \{x_i,y_i\}_{i = 1}^N$ I partition $\mathcal{D}$ into training set ($N-...
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21 views

Choosing perceptron weights to achieve 0% error

I'm really not sure what to do for this question, although I think I would be able to do q3 if I knew how to do q2
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0answers
12 views

Representative Pattern Extraction from Time Series using LSTM

I am interested in extracting a representative pattern from time series having variable time periods. I have attached an image for reference. I would like to know if a LSTM would be the appropriate ...
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2answers
28 views

Is DoE applicable to collect data for machine learning model?

I'm currently working on a machine learning model for a classification task in an engineering application. While working on this project I realized that the provided data is insufficient to get a ...
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1answer
21 views

advantage of using a stochastic Hopfield network model over a Boolean Hopfield network model

What is a Boolean Hopfield network model? is it same as binary hopfield network?
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0answers
12 views

Question regarding how to derive univariate Lasso closed form

In the picture we are given a lasso problem. The task is to derive equation (3) from equation (2). We do not need to show that the constraints hold for $\mathbf{w} $.I looked at the solution made by ...
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0answers
30 views

Dimensionality in Gaussian Process regression

I have a hard time understanding what it means that in Gaussian Process (GP) regression, every point is a new dimension. I'm reading the distill article which usually does a very good job explaining ...
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0answers
31 views

How does offset in XGBoost is handled in binary:logistic objective function

I am working on a mortality prediction (binary outcome) problem with “base mortality probability” as my offset in the XGboost problem. I have used gbtree booster and binary:logistic objective function....
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0answers
7 views

Learn to mimic function adaptively

Assume I have a function $F: R^n \to R$ that is slow to evaluate, which I, therefore, would like to approximate with something faster by using machine learning. I have seen some work proceeding by ...
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0answers
25 views

How to use a gaussian basis function

I am trying to build a linear regression model and one of my features when plotted against the output looks like a bell curve. So intuitively I figured that if I use the raw data for that feature my ...
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0answers
42 views

No Free Lunch in statistics [duplicate]

I was wondering if the No Free Lunch (NFL) theorem applies to even the estimation problem. Suppose there are $N$ points in the input. We are trying to estimate the mean value say weights associated ...
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1answer
26 views

Adaptive sliding window to detect varying time periods in time series dataset

I am looking for an algorithm that will allow me to segment time series with varying time periods. I have attached an image to better represent my intentions -
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0answers
23 views

Regression output scale

I am using XGBoost (gradient boosting) to predict the value of a continuous dependent variable The figure below shows a histogram of both the dependent variable data and the predicted data. (blue is ...
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1answer
49 views

Multivariate Time Series Classification/Regression

Background, I'm predicting stock price change direction (either up or down) with about 200 predictors. All of them are time series data. We have about 1500 days as training/validation data. My ...
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1answer
18 views

How to validate classification model with ordinal information

I have a Naive Bayes model that predicts 3 classes. As you increase each class it means that the condition is more severe. 0 means no condition, 1 is concern and 2 is that they have the condition. I ...
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1answer
30 views

Model Decreasing in Accuracy With More Training Data

I am training a tagger to predict whether or not a "word" is a proper noun or not. To do this I take in a list of "words" and their tags for part of speech. I then change all tags that aren't the tag ...
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0answers
20 views

Logistic regression: why MSE working better than Cross Entropy?

My model has 6 input features populated with continuous values (MinMax from -1 to 1) and 3 output. The aim is to mutually identify one of three classes (multiclass single label). I did tests for ...
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1answer
46 views

XGboost for Time series - using lag of target variables

I'm trying to make a time series forecast using XGBoost. I have already added many time related variables - day_of_week, month, week_of_month, holiday. I want to add lagged values of target variable ...
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0answers
18 views

Input/ouput design for deep reinforcement learning for imperfect information game

I'm working on a bot that plays an imperfect information game similar to chess, where each move you are effectively moving a piece from one location to another. I'm trying to decide what the best way ...
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1answer
38 views

disadvantages of svm

since I was reading about disadvantages of svm(support vector machine) Non-Probabilistic - Since the classifier works by placing objects above and below a classifying hyperplane, there is no ...
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1answer
40 views

Why is the value function obtained from a greedy policy different from its original value function (i.e. $ V_k \neq V_{\pi_k } $)?

Consider a vector of values $V_k$ and consider the related value $V_{\pi_k}$ obtained by coming the policy $\pi_k$ by acting greedily according to it. i.e. $$ \pi_k(i) := arg \min_{a \in A} \{ R(i,a) ...
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1answer
16 views

How to record performance of a glmnet model on a new dataset

I used cv.glmnet to create a model using one dataset ("Dataset 1"), but now I would like to look at performance (e.g., AUC) when predicting outcomes for new data ("Dataset 2"). I know that I can use ...
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2answers
86 views

Gaussian Processes: A Crucial Assumption?

I'm reading this paper, and I've come to what seems to be a pretty crucial assumption: Now, the n observations in an arbitrary data set, y = {y1, . . . , yn}, can always be imagined as a single ...
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1answer
42 views

The most appropriate model for dataset [closed]

For example, we have a dataset, and we want to find best representative hyperplane for this dataset. In other words, we aim to perform regression operation. This hyperplane can be in linear, ...
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1answer
29 views

Help me interpret my VGG16 fine-tuning results

I have a binary classification problem where I'm trying to classify whether a given cell is cancerous or not. For this I decided to play around with VGG16 pre-trained model and simply remove the last ...
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0answers
27 views

What is wrong with my logistic regression implementation?

Recently, I implemented the LR algorithm in Python. The main part of the code is as following(I didn't use mini batch in my code. Instead, I use the whole batch to compute gradients every time): <...
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1answer
27 views

Can I use machine learning to find similar and longer continuous time-series?

I have data which contains access duration of some items. Example: t0~t5 is the access time duration, 1 means the items was accessed in the time duration, 0 means it wasn't. ...