random-forest

问题 I am using scikit-learn's Random Forest Regressor to fit a random forest regressor on a dataset. Is it possible to interpret the output in a format where I can then implement the model fit without using scikit-learn or even Python? The solution would need to be implemented in a microcontroller or maybe even an FPGA. I am doing analysis and learning in Python but want to implement on a uC or FPGA. 回答1: You can check out graphviz, which uses 'dot language' for storing models (which is quite

问题 I want to improve the parameters of this GridSearchCV for a Random Forest Regressor . def Grid_Search_CV_RFR(X_train, y_train): from sklearn.model_selection import GridSearchCV from sklearn.model_selection import ShuffleSplit from sklearn.ensemble import RandomForestRegressor estimator = RandomForestRegressor() param_grid = { "n_estimators" : [10,20,30], "max_features" : ["auto", "sqrt", "log2"], "min_samples_split" : [2,4,8], "bootstrap": [True, False], } grid = GridSearchCV(estimator, param

问题 I got a question when training the forest. I used a 5-fold cross validation and rmse as guideline to figure out the best parameter for the model. I eventually find that when the max_feature=1, I got the smallest rmse. That's strange to me, since max_feature is the feature considered at each split. Generally, if I want to find the "best" parameter to lowest the impurity in splitting, the tree should, at best, consider all the features and find the one result in lowest impurity after splitting.

问题 This post is correlated with: How R automatically coerces character input to numeric? I am a user of the randomForest package. I just have a quick question: Can anyone let me know or refer me to the somewhere in the source code that how the randomForest package in R takes/treats character variables? I have used character variables as direct input and I also converted the character variables to factors as input, but the performances are different. Hope for a quick answer or a reference to

问题 I followed this tutorial to create a simple image classification script: https://blog.hyperiondev.com/index.php/2019/02/18/machine-learning/ train_data = scipy.io.loadmat('extra_32x32.mat') # extract the images and labels from the dictionary object X = train_data['X'] y = train_data['y'] X = X.reshape(X.shape[0]*X.shape[1]*X.shape[2],X.shape[3]).T y = y.reshape(y.shape[0],) X, y = shuffle(X, y, random_state=42) .... clf = RandomForestClassifier() print(clf) start_time = time.time()

问题 I followed this tutorial to create a simple image classification script: https://blog.hyperiondev.com/index.php/2019/02/18/machine-learning/ train_data = scipy.io.loadmat('extra_32x32.mat') # extract the images and labels from the dictionary object X = train_data['X'] y = train_data['y'] X = X.reshape(X.shape[0]*X.shape[1]*X.shape[2],X.shape[3]).T y = y.reshape(y.shape[0],) X, y = shuffle(X, y, random_state=42) .... clf = RandomForestClassifier() print(clf) start_time = time.time()

问题 I want to run a RF classification just like it's specified in 'randomForest' but still use the k-fold repeated cross validation method (code below). How do I stop caret from creating dummy variables out of my categorical ones? I read that this may be due to One-Hot-Encoding, but not sure how to change this. I would be very greatful for some example lines on how to fix this! database: > str(river) 'data.frame': 121 obs. of 13 variables: $ stat_bino : Factor w/ 2 levels "0","1": 2 2 1 1 2 2 2 2

问题 I am trying to use random forest in R for classifying some kaggle data but I keep getting the following error whenever I try to use the model which I have created. Error in predict.randomForest(fit, newdata = test, type = "class") : Type of predictors in new data do not match that of the training data I am totally lost as to the reason for this error and Google has not been of much help. Any help or insight will be appreciated. The simple code snippet is given below and its in response to one

问题 I have 15 features with a binary response variable and I am interested in predicting probabilities than 0 or 1 class labels. When I trained and tested the RF model with 500 trees, CV, balanced class weight, and balanced samples in the data frame, I achieved a good amount of accuracy and also good Brier score. As you can see in the image, the predicted probabilities values of class 1 on test data are in between 0 to 1. Here is the Histogram of predicted probabilities on test data: with

问题 I have 15 features with a binary response variable and I am interested in predicting probabilities than 0 or 1 class labels. When I trained and tested the RF model with 500 trees, CV, balanced class weight, and balanced samples in the data frame, I achieved a good amount of accuracy and also good Brier score. As you can see in the image, the predicted probabilities values of class 1 on test data are in between 0 to 1. Here is the Histogram of predicted probabilities on test data: with