why not mse for classification?
446909
Logistic Regression is a specific model with a specific loss function, if you use MSE, it is not called Logistic Regression any more. So let me try to answer "Why not use MSE for classification". ... Optimizing this loss function is hard because it is non-convex and discontinuous.
1265747
Support vector machines (SVMs) are formulated to solve a classical two class pattern recognition problem. We adapt SVM to face recognition by modifying the interpretation of the output of a SVM classifier and devising a representation of facial images that is concordant with a two class problem.
642654
"You cannot use any of the following on the exam: Mass vulnerability scanners (e.g. Nessus, NeXpose, OpenVAS, Canvas, Core Impact, SAINT, etc.) Any tools that perform similar functions as those above are also prohibited." ... Aren't Nmap, Nikto, Burp, and Dirbuser doing mass scans for vulnerabilities?
650908
['a) Classification. Classification derives a model to determine the class of an object based on its attributes. ... ', 'b) Prediction. Prediction task predicts the possible values of missing or future data. ... ', 'c) Time - Series Analysis. ... ', 'd) Association. ... ', 'e) Clustering. ... ', 'f) Summarization.']
913575
Standardised Mini-Mental State Examination (SMMSE) ... The MMSE was designed as a screening test for the purpose of evaluating cognitive impairment in older adults. The SMMSE was developed by Dr D.
30267
SE searches the information from its own database. MSEs Searches more hits for a single query, Meta Search Engine (Mamma, Dogpile, etc.) sends the user query to different search engines simultaneously and the top results shown as output to the user.
793132
['(1) No randomness of using some observations for training vs. ... ', "(2) As validation set is larger than in LOOCV, it gives less variability in test-error as more observations are used for each iteration's prediction."]
252273
It is difficult to represent real-world systems in terms of mathematical relationships. Data are often unavailable or inaccurate. Combining the sub- system models to create the model is seldom simple. Assumptions and estimates must be made at almost every step of the process.
1308191
Making MMD models is difficult! There is much to know and much to learn. There is no “magic”… you must become a student of the process in order...
254138
K-NN is a lazy learner because it doesn't learn a discriminative function from the training data but “memorizes” the training dataset instead. For example, the logistic regression algorithm learns its model weights (parameters) during training time. ... A lazy learner does not have a training phase.
951302
For multi class classification using SVM; It is NOT (one vs one) and NOT (one vs REST). Instead learn a two-class classifier where the feature vector is (x, y) where x is data and y is the correct label associated with the data.
1237403
Naive Bayes (NB) is 'naive' because it makes the assumption that features of a measurement are independent of each other. This is naive because it is (almost) never true.
1128432
R has a reputation of being hard to learn. Some of that is due to the fact that it is radically different from other analytics software. Some is an unavoidable byproduct of its extreme power and flexibility. And, as with any software, some is due to design decisions that, in hindsight, could have been better.
1216176
Regression and classification are categorized under the same umbrella of supervised machine learning. ... The main difference between them is that the output variable in regression is numerical (or continuous) while that for classification is categorical (or discrete).
251741
Model-Based Systems Engineering (MBSE) is the practice of developing a set of related system models that help define, design, analyze, and document the system under development. ... And they allow simulation of complex system and system-of-systems interactions with appropriate fidelity to accelerate learning.
1383263
mllib models: Cannot be trained without Spark cluster. Usually can be used for predictions without cluster, with exception to distributed models like ALS.
1145430
One of the primary reasons popular libraries SVM algorithms are slow is because they are not incremental. They require the entire dataset to be in RAM all at once. So if you have a million data points, it's going to run kind of slow.