random-forest

问题 This might look like a trivial problem. But I am getting stuck in predicting results from a model. My problem is like this: I have a dataset of shape 1000 x 19 (except target feature) but after one hot encoding it becomes 1000 x 141. Since I trained the model on the data which is of shape 1000 x 141, so I need data of shape 1 x 141 (at least) for prediction. I also know in python, I can make future prediction using model.predict(data) But, since I am getting data from an end user through a

问题 This might look like a trivial problem. But I am getting stuck in predicting results from a model. My problem is like this: I have a dataset of shape 1000 x 19 (except target feature) but after one hot encoding it becomes 1000 x 141. Since I trained the model on the data which is of shape 1000 x 141, so I need data of shape 1 x 141 (at least) for prediction. I also know in python, I can make future prediction using model.predict(data) But, since I am getting data from an end user through a

问题 This might look like a trivial problem. But I am getting stuck in predicting results from a model. My problem is like this: I have a dataset of shape 1000 x 19 (except target feature) but after one hot encoding it becomes 1000 x 141. Since I trained the model on the data which is of shape 1000 x 141, so I need data of shape 1 x 141 (at least) for prediction. I also know in python, I can make future prediction using model.predict(data) But, since I am getting data from an end user through a

问题 This might look like a trivial problem. But I am getting stuck in predicting results from a model. My problem is like this: I have a dataset of shape 1000 x 19 (except target feature) but after one hot encoding it becomes 1000 x 141. Since I trained the model on the data which is of shape 1000 x 141, so I need data of shape 1 x 141 (at least) for prediction. I also know in python, I can make future prediction using model.predict(data) But, since I am getting data from an end user through a

问题 I'm implementing a classification task with binary outcome using RandomForestClassifier and I know the importance of data preprocessing to improve the accuracy score. In particular, my dataset contains more than 100 features and almost 4000 instances and I want to perform a dimensionality reduction technique in order to avoid overfitting since there is an high presence of noise in the data. For these tasks I usually use a classical Feature Selection method (filters, wrappers, feature

问题 How is it possible to control the size of the subsample used for the training of each tree in the forest? According to the documentation of scikit-learn: A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=True (default

问题 How is it possible to control the size of the subsample used for the training of each tree in the forest? According to the documentation of scikit-learn: A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=True (default

问题 How is it possible to control the size of the subsample used for the training of each tree in the forest? According to the documentation of scikit-learn: A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=True (default

问题 How is it possible to control the size of the subsample used for the training of each tree in the forest? According to the documentation of scikit-learn: A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=True (default

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