Methods and principles of building "computer systems that automatically improve with experience."

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To interpret SVM's probability output in text analysis

So, my question is about the application of the SVM's (or naive Bayes') probability output (via Platt's scaling). I know the interpretation of the output is the probability of a given observation ...
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16 views

How to Format Data for Structured Learning Problem?

I'm working on a project classifying discussion forum posts into various pre-defined categories, and would like to use a sequential learning model such as CRF's. I code mostly in Python and have found ...
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35 views

analysis of bank account record

I am new to the field of time series analysis, but I would like to have a look at my bank account and determine my spending habits. I read a lot about clustering of multiple time series but I think I ...
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33 views

What do these decision boundaries indicate in random forest and svm?

I was working on data science harvard homework problem. It is a two class classification problem in which they plot the decision boundary for random forest, svm and decision tree. The problem has 2 ...
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1answer
47 views

Are visualization techniques useful when the predictive model is a highly flexible machine learning algorithm?

I’ve always used highly flexible machine learning algorithms like boosted trees, support vector machines, and random forests that supposedly excel at identifying non-linear and irregular patterns and ...
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1answer
25 views

Understanding the effect of hyperparameters in machine learning experiments

In machine learning every algorithm has a set of hyperparameters which needs to be optimized for best prediction performance. The simplest method for this optimization is called grid search which ...
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1answer
42 views

Conditional distribution for Exponential family

We have a random variable $X$ that belongs to the exponential family with p.d.f. $$ P_X(x|\boldsymbol \theta) = h(x) \exp\left(\eta({\boldsymbol \theta}) . T(x) - A({\boldsymbol \theta}) \right) $$ ...
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40 views

determining how “important” a feature is in predicting a target in decision trees

Random forests allow us to compute a heuristic for determining how "important" a feature is in predicting a target. This heuristic measures the change in prediction accuracy if we take a given ...
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32 views

In developing a personal assistant, what is the best classification machine learning algorithm to use? [closed]

I've been developing a personal assistant-type program (siri-esque, I guess), which currently uses regexes loosely matching text to infer the requested operation, but as you can imagine, as it grows, ...
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44 views

Any machine Learning models to predict dates?

I have a general question regarding machine learning models. The idea is to predict what DATE the customer is likely to make transactions or purchases. Variables present in the data set are item, ...
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17 views

Early electric perceptron [closed]

As I was reading up about the perceptron, wikipedia has a tidbit that says an early prototype of the perceptron had potentiometers for weights and servos to change the weights. Would any one have a ...
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1answer
45 views

Leave-One-Subject-Out cv method

I would like to use a Leave-One-Subject-Out cv on my datasets (I have dataset including 38, 15, 10 participants, respectively). I don't know the hyperparamenters C and gamma of my SVM so I have to ...
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56 views

How to handle data normalization in kNN when new test data is received

I had a discussion with my colleagues about the following problem: Lets say we have 100 points of labeled data and we are using $k$-nearest neighbor method for prediction. So our data looks like ...
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50 views

VC-Dimension of k-nearest neighbor

What is the VC-Dimension of the k-nearest neighbor algorithm if k is equal to the number of training points used? Context: This question was asked in a course I take and the answer given there was ...
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17 views

Multiple Transformations of a Dependent Variable

Assume you have a data set with a continuous output variable (y), five continuous predictors (x1, x2, x3, x4, and x5), and a few additional categorical predictors. x1 through x4 have a weak ...
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1answer
23 views

Is the solution of SVM classifier a vector in second conjugate space of the RKHS

Let the training points be given by $x_1, x_2 \cdots x_m$. Suppose we want to predict the class of a new point $x$ as $f(x)$. In an linear SVM this is a dot product usually denoted by $<w,x>$ ...
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State of the Art versions of Generalized Additive Models

Generalized Additive Models [Tribshirani 86] was well received with over 1335 Citations. I am also aware of the popular version of GAM - the Multivariate Adaptive Regression Splines [MARS by Friedman ...
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457 views

Difference between Bias and Error?

In statistics, what is the difference between Bias and Error? You can say, Bias is a type of error? or Bias is an error with some tendency?
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22 views

Selecting the best subset of features in binary logistic regression [duplicate]

I am using a binary logistic regression (a type of probabilistic statistical classification model, is used to predict a likelihood of belonging to a class (True, False)). I have 4 features and I want ...
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40 views

Real time updating of training data and classification model

Setup: I have a couple of binary classification models based on Logistic Regression and Gradient Boosted Trees. Currently I train the model offline and use it to predict the class of incoming data. ...
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2answers
28 views

Classification algorithms in R that accept training instances with weights

Are there any classification algorithms implementations in R such as Decision Trees, Naive Bayes, etc. in which the training instances can have a weight? I found this relevant question in ...
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2answers
137 views

Machine learning feature encoding

I'm new to Machine Learning. I've just finished the Coursera course. :) And for my first practical attempt I wanted to "analyse" a local used cars selling website in order to compose a modal that ...
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1answer
55 views

Perceptrons and Decision Boundaries

I am currently studying neural networks and have been trying to reason about this for a while to no avail. I understand that given a perceptron(such as above) with f as a step function, any ...
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30 views

Correctness proof of bayesian networks

I want something simple and my brain is getting in my way. Assume I have three different coins - C1 is fair, C2 has p(Heads)=0.6 and C3 has P(haeds)=0.8 I want to draw a bayes network for the ...
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15 views

How to extract the feature and which ML method should be used

There will be seom bidding info. It need to tell which bidding is from which team. Or how other team will bid. The market will give the total demand every 0.5h. Then each team will offer their price ...
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14 views

SGD weight learning for mixture of generative models

Let's say that I've learned $k$ generative models $\mathbf{G}=G_i$ in some ways from my data. I'd like to create a mixture from them in order to have something like model averaging in this form: ...
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29 views

Pre-processing (center, scale, impute) among training sets (different forms) and the test set - what is a good approach?

I am currently working on a multi-class classification problem with a large training set. However, it has some specific characteristics, which induced me to experiment with it, resulting in few ...
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41 views

What is the name of this validation procedure?

I have a set of classified data. In order to test the precision of several algorithms- I split the data into train and test sets. For the test set I choose at random 30% of the data and the rest is ...
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1answer
54 views

machine learning with linear regression algorithm

I'm noob in machine learning, but I'm trying to know more about it. I have a question about a prediction model (predict for every page when the number of click). I try to use kNNimpute to handle with ...
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8 views

Do class-conditional densities always have to sum to 1 conditional on the classes? [duplicate]

I read a lesson here: http://www.byclb.com/TR/Tutorials/neural_networks/ch4_1.htm, and noticed that the class-conditional densities did not sum to one (if we drew vertical lines in figure 4.1, the ...
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26 views

Prediction using categorical, binary and time series variables

I have per subject: categorical variables - ex: grade, mother_education continuous variables - ex: ...
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41 views

Hidden Markov Model vs Markov Transition Model vs State-Space Model…?

For my master's thesis, I am working on developing a statistical model for the transitions between different states, defined by serological status. For now, I won't give too many details into this ...
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1answer
17 views

Decision Tree English Rules and Dependency Network in MS SSAS

I created a Decision Tree model in Microsoft Analysis Services (SSAS, Visual Studio 2010). There are two tabs in the Mining Model Viewer tab: (1) Decision Tree that shows a tree itself, and (2) ...
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63 views

Precision, Recall, and PR Curve

I am doing research on binary classification. I have three classification models work on one data set. I have no True Positive, True Negative, False Positive, and False Negative metrics, each model ...
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1answer
45 views

How to measure co-adaptation occuring in a multi-layer perceptron neural network that does not use a drop out?

The dropout proposed by Hinton is said to prevent co-adaptation. My question is how can I measure the co-adaptation that occurs in a multi-layer perceptron that does not use a drop out?
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32 views

Weekly data normalization - Python

I have a weekly dataset and I have to normalize this data. Data is something like this : ...
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1answer
34 views

How to know if my data is balanced or imbalanced for an ROC curve analysis?

I am doing a research on the reliability of different models in detecting hidden defects in a test specimen. I have made a test specimen with defect prevalence about 25% (12 positive out of 49 total ...
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1answer
54 views

Is there any neural network whose output can be probabilistic, just like multi-class logistic regression?

I want to add nonlinear character into multi-class logistic regression. I know kernel logistic regression can do it. Is there any kind of neural network which has similar characteristic?
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2answers
42 views

What is the proper name of a model that takes as input the output of another model?

Thanks in advance for the help. I am writing a paper and for the life of me can't remember the proper term for a model that works as follows. ...
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1answer
42 views

Questions about weather prediction in scikit learn

Hello I am a high school student doing research on weather. I have a dataset that has four columns each labeled with time, pressure, and lat/long. I am confused on the cross validation process. What ...
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1answer
43 views

How are individual trees added together in boosted regression tree?

I'm reading Introduction to Statistical Learning, James, G., et al. (2013), in which they describe the Boosted Regression Tree algorithm as following. What I do not understand is Eq 8.10 and 8.11. ...
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1answer
55 views

Problem with getting neural network learned to calculate XOR

I am learning about neural networks. I found a course on Coursera about machine learning https://www.coursera.org/course/ml . What I am trying to implement is a neural network to calculate logical ...
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28 views

How to do ranking with scikit-learn random forest model

I have a training dataset that I've developed, that has the following format: ------------------------------ | User ID | Item | Label | ------------------------------ | 001 | umbrella | 0 ...
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52 views

Can a Naive Bayes Model predict using pattern alone?

Say I have a set of data abc-def-ghi jkl-mno-pqr stu-vwx-yza and lots of other training samples which are catagorized as **names*. The above dataset does not have ...
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1answer
33 views

Is this a job for mixture of experts regression or semi-hidden markov models or something else?

Data I have several thousand timeseries each comprising around 365 data points. Browsing through a few of them, it looks like each timeseries consists of several regimes (different number f regimes ...
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26 views

Model selection and performance evaluation using cross-validation for time series with missing values

So my task is to select and evaluate a statistical model (random forest, boosted trees, neural networks etc.) for a time series with missing values around 10 years long. One of the goals of that is to ...
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30 views

Obtain Precision and Recall from Click through data

I am trying to build a graph of precision and recall using click data. I have two data sources. First data source has all the user clicked item_ids based on a given query_id. Second data source has ...
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Creating a model for a webshop

I'm going to create a Multi-armed bandit algorithm to handle recommendations for a large scale webshop. I'm going to use Thompson sampling (http://en.wikipedia.org/wiki/Thompson_sampling) and would ...
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62 views

Why can we assume that samples $X_i$'s are independent if the parameter is fixed (though unknown)?

To put it in context, I was trying to learn Bayesian parameter estimation (by an example of learning the probability of heads of a coin) and was trying to understand the independence of the samples ...
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68 views

What gradient descent method is better for convolutional neural network?

Let's say we want to train a convolutional neural network, what gradient descent method works better? (1) Batch gradient descend (2) stochastic gradient descent