Classification Questions

I have in a web application's pom file, a build profile, which does some necessary things (in my code) for qa testing. i have this code on

I'm pretty new with skmultilearn, now i use this for 'chinese' documents multiple label classification. the training dataset is quite small(like

I am trying to solve the knapsack problem by applying my own algorithm. i give each item a score (values[i] - weights[i]) and add high score items

I have a dataset with 1000 data points. each data point is assigned label 1 or 0 as follows. my

I am using sklearn's support vector machine (svc) as follows to get the prediction probability of my instances in my dataset as

I am currently trying to classify a bunch of rivers in regard to their behavior. many of the rivers have a behavior that is very similar to a

In relation to the previous post on stackoverflow

I am trying to get top n predicted labels of a text based multi-label classification problem as a list for a particular record. i have

I have a time-series dataset as follows where i record 2 time-series for each of my sensors. the label column depicts if the sensor

I'm working on a personal project to build a text classifier. i scraped around 3000 news articles from 8 categories. i have every single word in

Oner, short for "one rule", is a simple yet accurate classification algorithm that generates one rule for each predictor in the data, then selects

I have a data like this

I'm going dialect text classification and i have this code: from sklearn.naive_bayes import multinomialnb from

I'm trying to classify time series data using sql. i have data for a reference data point that occurs over 3 years. so the reference occurs 36

I am trying to do the machine learning practice problem of loan prediction from analytics vidhya. when i am using a random forest classifier, it

I am using the example here to classify

I have two variables x and y. the structure of x (i.e an np.array): [[26777 24918 26821 ... -1 -1 -1] [26777

I'm fairly new to nlp and trying to learn the techniques that can help me get my job done. here is my task: i have to classify stages of

I have two separate files, one is a text file, with each line being a single text. the other file contains the class label of that corresponding

I am trying to classify data based on prespecified labels. got two columns and shown below: room_class

I want to get prediction probabilities for each class of each output. but the classes_ attribute does not exist on the

I am working on a data set of approximately 3000 questions and i want to perform intent classification. the data set is not labelled

I am trying to apply reiforcement learning mechanism to classification tasks. i know it is useless thing to do because deep learning can

I have user review dataset like review-1, 0,1,1,0,0 review-1 is user review and

I am working on classification of brain tumors. the dataset is comprised of brain images from various angles, with a border and mask of the tumor

I am trying to train a model for sentiment analysis and below is my trained multinomial naive bayes classifier returning an accuracy of 84%.

In mnist data set, there's 10 classification output. now, i like to using svm as a classifier for this classification task. i

I have a classification problem where i need to predict a class of (0,1) given a data. basically i have a dataset with more than 300 features

I made my first cnn using tensorflow. the objective is to recognize if there is a dog or a cat in a black-white photo with 100x100 pixels. the

I am working on a text classification problem where multiple text features and need to build a model to predict salary range. please refer the

I'm trying to use multiclass svm code from mblondel

I am still a beginner studying machine learning for the first time. i am dealing with problem of 4 class classification in my research. i would

I have the following code for an image classification problem. and i keep running into this error: valueerror: error when checking

My question is: how can i transform a data frame like this to eventually use it in scikit's mulitlabelbinarizer: d1 =

Question how to batch train a multi-step lstm in keras for single-label multi-class classificaiton, at each time-step for

I am trying to create a multiclass text classifier as explained

I am very new to datascience/pandas in general. i mainly followed

I have the following simple neural network (with 1 neuron only) to test the computation precision of sigmoid activation &

The code for classification of mnist dataset using transfer learning is given in the link

I have x as text, with two different labels(columns) to train. --input.csv-- content, category, rate text test, 1, 3 new test, 2,

I have a numpy array like this: [[0. 1. 1. ... 0. 0. 1.] [0. 0. 0. ... 0. 0. 1.] [0. 0. 1. ... 0. 0. 0.] ... [0. 0. 0. ... 0.

I build a keras model that has 2 branches, each taking a different feature representation for the same data. the task is classifying sentences

Given a model that has to classify 10 table-cloth items (spoons, forks, cups, plate etc,) and must be tested on an image of a table with all the

I am getting the error in the post title when i attempt to run a pretrained mobilenet classification. the image i am using to run the script is

How do you compute the true- and false- positive rates of a multi-class classification problem? say, y_true = [1, -1, 0, 0, 1,

In tensorflow, tf.argmax() returns the index of the greatest element in an array. however, for multi-label classification tasks, a

I'm trying to implement a custom kernel, precisely the exponential chi-squared kernel, to pass as parameter to sklearn svm function, but when i

I am pretty new in machine learning, so i still don't understand how i can visualize the border between 2 classes in bag of words case. i

Is it possible to convert a string vector into an indexed one using numpy ? suppose i have an array of strings like

While working with gradientboostingclassifiers, i'm trying to obtain the probability of the classes the model has predicted for which i use the

This is my code loaded the pre-trained weights and embedding matrices from __future__ import print_function import numpy as np

I am trying to modify cross validation code developed by weka in this

I wrote this code and wanted to obtain probabilities of classification. from sklearn import svm x = [[0, 0], [10,

I am experimenting in sklearn learn classification with some nltk type tutorials. can someone help me understand why the

I do understand the reason for the conversion in dnn part because we have to convert our features into numbers to feed the network. however, i did

I have following time series data.i want to use classification model.for independent variable i want to pass an array of previous 5 values of

I have this image.

I used the following to make predictions from my keras model: # fit the keras model on the dataset ker_model.fit(xtrain, ytrain,

I wrote a very simple multiclass classifier based on the iris dataset. this is the code: import numpy as np from sklearn.datasets

Using make_classification from sklearn for a binary classification problem, i get labels 0 and

I am trying to implement a simple naive bayes classifier, on training i observed that if keywords (prediction) belongs to both class

This is a simple example of classification_report in sklearn from sklearn.metrics import

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My question is, does the machine learning algorithm takes care of selecting the best features in my data ? or shall i do