classification

问题 The ID3 algorithm uses "Information Gain" measure. The C4.5 uses "Gain Ratio" measure which is Information Gain divided by SplitInfo , whereas SplitInfo is high for a split where records split evenly between different outcomes and low otherwise. My question is: How does this help to solve the problem that Information Gain is biased towards splits with many outcomes? I can't see the reason. SplitInfo doesn't even take into account the number of outcomes, just the distribution of records in the

问题 I've been trying to validate my code to calculate Mahalanobis distance written in Python (and double check to compare the result in OpenCV) My data points are of 1 dimension each (5 rows x 1 column). In OpenCV (C++) , I was successful in calculating the Mahalanobis distance when the dimension of a data point was with above dimensions. The following code was unsuccessful in calculating Mahalanobis distance when dimension of the matrix was 5 rows x 1 column. But it works when the number of

问题 I am currently looking for a way i can combine the output of multiple model into one model, I need to create a CNN network that does classification. The image is separated into sections (as seen by the colors), each section is given as input to a certain model (1,2,3,4) the structure of each model is the same, but each section is given to a separate model to ensure that the the same weight is not applied on whole image - My attempt to avoid full weight sharing, and keeping the weight sharing

问题 I am aware of the concept of Precision as well as the concept of Recall. But I am finding it very hard to understand the idea of a 'threshold' which makes any P-R curve possible. Imagine I have a model to build that predicts the re-occurrence (yes or no) of cancer in patients using some decent classification algorithm on relevant features. I split my data for training and testing. Lets say I trained the model using the train data and got my Precision and Recall metrics using the test data.

问题 I am aware of the concept of Precision as well as the concept of Recall. But I am finding it very hard to understand the idea of a 'threshold' which makes any P-R curve possible. Imagine I have a model to build that predicts the re-occurrence (yes or no) of cancer in patients using some decent classification algorithm on relevant features. I split my data for training and testing. Lets say I trained the model using the train data and got my Precision and Recall metrics using the test data.

问题 I have some test data and labels: testZ = [0.25, 0.29, 0.62, 0.27, 0.82, 1.18, 0.93, 0.54, 0.78, 0.31, 1.11, 1.08, 1.02]; testY = [1 1 1 1 1 2 2 2 2 2 2 2 2]; I then sort them: [sZ, ind] = sort(testZ); %%Sorts Z, and gets indexes of Z sY = testY(ind); %%Sorts Y by index [N, n] = size(testZ'); This will then give the sorted Y data. At each element of the sorted Y data, I want to classify each point to the left as being of type 1 and everything to the right being class 2; This will then be

问题 I am trying to build an RNN/LSTM model for binary classification 0 or 1 a sample of my dataset (patient number, time in mill/sec., normalization of X Y and Z, kurtosis, skewness, pitch, roll and yaw, label) respectively. 1,15,-0.248010047716,0.00378335508419,-0.0152548459993,-86.3738760481,0.872322164158,-3.51314800063,0 1,31,-0.248010047716,0.00378335508419,-0.0152548459993,-86.3738760481,0.872322164158,-3.51314800063,0 1,46,-0.267422664673,0.0051143782875,-0.0191247001961,-85.7662354031,1

问题 I am new to Mahout. I have a requirement to convert a text file to a vector for classification in later stage. Could anybody of of shed some light on these below questions? How to convert a text file to a vector in mahout? The file format is like "username|comment about item|rating" The data will be few TBs. So which algorithm implementable I can use for classification using the vector I suppose to create? Thanks, Arun 回答1: You can check these 2 examples that also somewhat do/explain how to

问题 I am extracting image features from 10 classes with 1000 images each. Since there are 50 features that I can extract, I am thinking of finding the best feature combination to use here. Training, validation and test sets are divided as follows: Training set = 70% Validation set = 15% Test set = 15% I use forward feature selection on the validation set to find the best feature combination and finally use the test set to check the overall accuracy. Could someone please tell me whether I am doing

问题 When doing regression or classification, what is the correct (or better) way to preprocess the data? Normalize the data -> PCA -> training PCA -> normalize PCA output -> training Normalize the data -> PCA -> normalize PCA output -> training Which of the above is more correct, or is the "standardized" way to preprocess the data? By "normalize" I mean either standardization, linear scaling or some other techniques. 回答1: You should normalize the data before doing PCA. For example, consider the