The below is a picture which denotes the error of an ensemble classifier. Can someone help me understand the notation What does it mean to have (25 and i) in brackets and what is ε^1 is it error of first classifier or the error rate raised to power i. Can someone explain this formulae.

I have been implementing a DecisionTreeRegressor model in Anaconda environment with a data set sourced from a 20 million row, 12-dimensional CSV file. I could get the chunks off of the data set with chunksize set to 500,000 rows and process the computation of the R-Squared score on the training/test split data sets in each iteration of 500,000 rows till iteration #20. sklearn.__version__: 0.19.0 pandas.__version__: 0.20.3 numpy.__version__: 1.13.1 The GridSearchCV() instance uses parameter grid with parameter max_depth set to values …

Let's say I'm building an app like Uber and I want to predict the user's most likely destination based on the user's past history, current latitude/longitude, and time/date. Here is the proposed architecture - Let's say I have a pre-trained model hosted as a service. The part I'm struggling with is, how do I get the user features from the database in realtime from the RiderID to be used by the prediction service (XGBoost Model)? I'm guessing a lookup in …

I am trying to make an ensemble model composed of two pre-trained models, using torch, in order to classify an image. Below is some code, based on this post. import timm import torch from torch.nn import functional as F num_classes = 100 model1 = timm.create_model("efficientnet_b0", num_classes=num_classes) checkpoint1 = torch.load(checkpoint_path1) model1.load_state_dict(checkpoint1["model"]) model2 = timm.create_model("efficientnet_b2", num_classes=num_classes) checkpoint2 = torch.load(checkpoint_path2) model2.load_state_dict(checkpoint2["model"]) class EnsembleModel(nn.Module): def __init__(self, modelA, modelB, num_features): super().__init__() self.modelA = modelA self.modelB = modelB self.classifier = nn.Linear(2*num_features, num_features) def forward(self, x): x1 …

I am building a 3 neural network models on dataset that is already separated to train and test sets. From my analysis, I found that this dataset has values on test set which don't exist in the train set. And this gives a certain limitation or maximum capacity to my neural network model(s). By this I mean, I can not seem to improve the accuracy even if I change the hyper parameters or the parameters of my models. I have …

I have a classification problem where I am trying to predict if the data returns a 1 or 0. So your classic binary classification. I have my set of data that I have split into the dependent variables (ones I am training on) and the independent variable (my target that I am predicting, either a 0 or 1). I am using log loss as the scoring metric for my model. Firstly, I am using the cv function in xgboost to …

I was wondering about whether it is possible to train a ML model for a classification task with dataset D, and then train another model to solve the same classification task, which takes as input dataset D and the output of the first model. I am worried about the data partition in this case (i.e. whether the models need to be trained and tested with different data samples). If this kind of ensemble method exists, how is it called? As …

I'm new to datascience and appreciate your sage advice! I need to build an incremental learning model, and I know there's a lot that goes into something like that, but I'd like to highlight the most fundamental, abstract requirement I have in my particular case and ask you to focus your attention on that. However, I build the incremental learning model it must never forget what it has learned. That is when it learns something new it can't forget something …

I'm not completly sure about the bias/variance of boosted decision trees (LightGBM especially), thus I wonder if we generally would expect a performance boost by creating an ensemble of multiple LightGBM models, just like with Random Forest?

I presently have 2 algorithms that have a numerical output. Using a threshold of 0.9, I get the classification output. Let's say they are: P (high precision, low recall) R (high recall, low precision) Individually, they have poor F-1 scores. Is the naive way of creating a classifier C as: C(*) = x.P(*) + (1-x).R(*) And optimizing for x and threshold a good approach to improve the F-1 score? Or is there some alternate approach I must try. Note: I …

I have multiple models predicting an outcome (continuous) and I want to take action to optimize the best values of these features to make a decision. Consider a regression model, y1 = m1x1 + m2x2 + m3x3 + k and another mode preponsity model y2 = P[1 | x5, x6, x3] = 1 / (1 + exp(-m5x5 - m6x2 - m7x3)) I would want to maximize y3 say y3 = f(y1, y2) by finding the optimal values of the features …

I'm looking at the best way of combining CNN with image input and a scalar value. I know that one of the ways is to concatenate flatten layer with this scalar value. But flatten layer consist for example 2048 such scalar values with different magnitude than a single input value. And what if in a real task this scalar value has more influence than image. Also one of the examples is a combination of a text and image and then …

I'm studying ensemble learning methods, focus on random forest and gradient boost. I read this article about this topic and this about meta learning. It is possible to say that ensemble learning is a subset of meta learning?

I have three models: Arima Auto ARIMA Double Exponential Smoothing I would like to apply an ensemble method - a voting method and allow the classifier to learn weights for these three models. I have checked the votingclassifier present in scikit learn. It requires: fit(x,y) to run. Time series object that is present in series object don't have y. How do you apply a voting classifier and learn weights through grid search?

I have a dataset where some features (c,d) apply to only when a feature (a) is a specific value. For example a, b, c, d T, 60, 0x018, 3252002711 U, 167, , U, 67, , T, 66, 0x018, 15556 So I'm planning to splitting the dataset so that there are no missing values. a, b, c, d T, 60, 0x018, 3252002711 T, 66, 0x018, 15556 a, b U, 167 U, 67 and then put these into individual models which combine …

I'm working on a regression model using Boosting algorithms (CatBoost, XGBoost, and LightGBM). All models give similar accuracy of 0.2 RMSE (Target varies from 0 to 1). I obtained the following plots when I plotted residues. My model is overpredicting for small target value (near zero) and underpredicting for large target value (near 1). How can I improve my model performance? The model is not overfitting and I'm doing an exhaustive hyperparameter search and basic feature engineering. I'm trying to …

For example, say I am trying to predict whether I will win my next pickleball game. Some features I have are the number of hits, how much water I’ve drinken, etc, and the duration of the match. I’m asking specifically for ensemble models but will extend this question to other scenarios, what format would the duration column best be in? (e.g. milliseconds, seconds, minutes (integer), minutes (float), one column for minutes and one column for seconds, etc)

I tried to do multi-class classification problem. The goal is to predict whether the match will be won by HomeTeam, AwayTeam or Draw. I did feature engineering from the attributes and finally came up with final data to train a classifier. I make sure that the data is balanced for all the 3 class. To train a classifier I did XGB Classifier, Logistic Regression, SGD Classifier and Normal DNN(Tensorflow Estimator). I checked the metrics for all the classifiers and I …

I am trying to implement stacking model for a ML problem and having hard time figuring out the cross validation strategy. So far I have used 10-fold cross validation for all my models and would like continue using that stacking as well. Here's what I came up with but not sure if it makes sense, At each iteration of 10-fold CV, you will have 9 folds for training (training dataset) and 1 fold for testing (testing dataset). Divide the training …

I have a bunch of small neural networks (say, 5 to 50 feed-forward neural networks with only two hidden layers with 10-100 neurons each), which differ only in the weight initialization. I want to train them all on the same, smallish dataset (say, 10K rows), with a batch size of 1. The aim of this is to combine them into an ensemble by averaging the results. Now, of course I can build the whole ensemble as one neural network in …