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Accuracy is the worst metric you could use for an imbalanced dataset. If you choose accuracy as a metric when you have class imbalance, you will get very high accuracy. This is because the majority class has a higher frequency (or has more number of records) and hence the model will predict the majority class as the prediction majority of the time. The ...


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TL;DR: 5.1% top-5 classification error (expert #1), but 2.4% optimistically. In the paper ImageNet Large Scale Visual Recognition Challenge by Russakovsky et at. (2014), there is a section in which they estimated the human classification error for ILSVRC (Section 6.4.1 Quantitative comparison of human and computer accuracy on large-scale image ...


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What you are talking about is called feature engineering. Basically it is done to reduce the dimensionality of the dataset. What we are doing is combining 2 or more features which provide the same info, into one feature. For example I had this dataset where I had to predict the price of a used car. There were 2 features month of registration and year of ...


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The term learning curve can mean different things in different context, which is confusing. When talking about neural networks (and other iteratively trained models) the learning curve describes the model's training progress. It is often used to determine when it's time to stop training. In scikit-learn, the learning curve is interpreted differently. It ...


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It is correct that calling learning_curve will refit your model multiple times for different training dataset sizes. You can simply pass specific hyperparameters when initializing the model you want to use, which you can then pass to learning_curve for the estimator argument. The actual loss funtion that is used depends on the type of estimator you are ...


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This lies in the definition of Grid Search.Grid-search is used to find the optimal hyperparameters of a model which results in the most ‘accurate’ predictions.I dont think there ia any kind of abnormality associated with the final prediction . However Accuracy is not the only metrics to evaluate our Classification models. Use Confusion Matrix to evaluate ...


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One way to compare models is to look at the different decision boundaries the different models have learned. The different decision boundaries can impact the evaluation metrics.


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A few thoughts: The first thing I would check is whether the other models overfit. You could check this by comparing the performance between the training set and the test set. Also there's something a bit strange about k-NN always predicting the majority class. This would happen only if any instance is always closer to more majority instances than minority ...


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From your comment above, " though Grid & Radom Searches are expected to do better." They are EXPECTED to perform better but it is not a given that in each and every case they will outperform K Fold CV. Sometimes K FoldCV can outperform Grid or Random SearchCV.


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Accuracy treats all misclassifications as the same - we only care whether we got the answer right or not, but don't care about what kind of error was made. Even for class-balanced problems, this may not be a desirable feature. If misclassifications come with different "costs", accuracy is not a good measure of the overall utility of your classifier....


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Your data is multidimensional, it is possible that any two dimensional projection overlaps while still existing an hyperplane on the original dimensionality that separates the two classes well Say for instance you have 3 data points from 2 labels in 2d that are linearly separable X:(0,-1) O:(1,2) X:(4,3) X O X In the x axis they look ...


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