I have a data set which contains a list of stock prices. I need to use the tensorflow and python to predict the close price. Q1: I have the following code which takes the first 2000 records as training and 2001 to 20000 records as test but I don't kn
I am following a this tutorial to write a Naive Bayes Classifier: http://machinelearningmastery.com/naive-bayes-classifier-scratch-python/ I keep getting this error: dataset[i] = [float(x) for x in dataset[i]] ValueError: could not convert string to
I wrote neural-network using tensorflow tools. everything working and now I want to export the final weights of my neural network to make a single prediction method. How can I do this?You will need to save your model at the end of training by using t
I am unable to understand this http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler Can anyone explain this to me in simple terms?The idea behind StandardScaler is that it wil
For my assignment I need to make a Machine Learning program which does the following: As input the program gets a building plan (written in text in PDF) for a project, mainly bridges and sluices. The machine learning program takes every sentence in t
I am using python and scikit-learn's tree classifier in a little fictive machine learning problem. I have binary outcome variable (wc_measure) and I believe it is dependent on a few other variables (cash, crisis, and industry). I tried the following:
I have many sets of related data that I want to use to train a neural network. The data is from racing pigeons that fly a set distance. The inputs could be weight, age, size, wing span, sex, distance, time etc. sampled lets say every minute. I am try
Due to certain reasons, I'm using 2 different VW releases: the latest development version (8.1.1) for my experiments, and the "latest stable" 7.10 release for production. So, the question is: what happens if I simply specify -c flag for the prod
Most classification algorithms are developed to improve the training speed. However, is there any classifier or algorithm focusing on the decision making speed(low computation complexity and simple realizable structure)? I can get enough training dat
Well I googled about Content Based Image Retrieval(CBIR) and actually the wiki definition is quite clear but there is not much material nor books related to subject. Can anyone explain what are the components that comprise Content Based Image Retriev
I need to keep track of the F1-scores while tuning C & Sigma in SVM, For example the following code keeps track of the Accuracy, I need to change it to F1-Score but I was not able to do that--. %# read some training data [labels,data] = libsvmread('.
I am using svm-rank. When running svm_rank_learn on a tiny dataset: Training set properties: 3 features, 12 rankings, 596 examples The run finishes in a few seconds and I get a valid model. But when I use a bit larger dataset: Training set properties
I have a decision tree that is trained on the columns (Age, Sex, Time, Day, Views,Clicks) which gets classified into two classes - Yes or No - which represents buying decision for an item X. Using these values, I'm trying to predict the probability o
I am making a chat bot for my sister in batch but it is consuming so much time I figured I would let it have her tell it what to say when it does not know. However I can not get it working and I figured someone on here might know. Here is what I have
I have come across an extract from an old paper which casually mentions, If required, we could use KMeans as a method of asserting that this dataset is noisy, thus proving that our classifier working as well as can be reasonably expected. I can find
I am using python sklearn library for doing classification of data. Following is the code I have implemented. I just want to ask, is this correct way of classifying? I mean can following code potentially remove all the biases? And, is it 10-k fold cr
I work with some research educational task and need dataset with classified facial emotions to train classifier. For example, gender classification is simple: I can create csv file, and mark any file with image as 0 or 1, according to gender. Somethi
Using a classsication algorythm (for example naive bayes or SVM), and StringToWordVector, would it be possible to use TF/IDF and to count terms frequency in the whole current class instead of just looking in a single document? Let me explain, I would
I have a list of customers and features in the following format: UserID, Feature1, Feature2, Feature3, Feature4 So I have a list -- called "Customers" -- and it looks like this: [ ['975676924', '1345207523', '-1953633084', '-2041119774', '587903
I'm starting to learn some stuff about big data with a big focus on predictive analysis and for that I have a case study I would like to implement: I have a dataset of servers health information that is polled every 5sec. I want to show the data that
I'm going through the ML Class on Coursera on Logistic Regression and also the Manning Book Machine Learning in Action. I'm trying to learn by implementing everything in Python. I'm not able to understand the difference between the cost function and
Whats the fastest way to get an approximate count of number of rows of an input file or std out data stream. FYI, this is a probabilistic algorithm, I can't find many examples online. The data could just be one or 2 columns coming from an awk script
I have my own java based implementation of clustering (knn). However I am facing scalability issues. I do not plan to use Mahout because my requirements are very simple and mahout requires lot of work. I am looking for java based Canopy clustering im
This is my first post on StackOverflow, so apologies if it's lacking the right information. Scenario. I'm in the process of moving away from the Google Weather API to BOM (Australia) weather service. I've managed to get the weather data from BOM just
I am using weka for classification. In weka i am using SMO to classify the documents.In some situation SMO return wrong category. For example take 2 category Computer and Cricket.First i trained and created model for these 2 category.Then i am going
Is there a training and optimization algorithm for 2-D (two dimensional) conditional random fields (CRF) suited for classification of imagery? Has anyone used CRF package in R (http://crf.r-forge.r-project.org/html/CRF-package.html) for image classif
I've been coding for a few years but I still haven't gotten the hang of pseudo-coding or actually thinking things out in code yet. Due to this problem, I'm having trouble figuring out exactly what to do in creating a learning Decision Tree. Here are
I am aware of the duplicates of this question: How does the Google "Did you mean?" Algorithm work? How do you implement a "Did you mean"? ... and many others. These questions are interested in how the algorithm actually works. My quest
I decided to go with a Neural Network in order to create behaviors for an animation engine that I have. The neural network takes in 3 vector3s and 1 Euler angle for every body part that I have. The first vector3 is the position, the second is its vel
I have a problem to detect object in images or video frames. I have a task that is detect some people or something who enter into the sight of web camera, and then my system will be alarm. Next step is recognize which kind of thing the object is, in
