So this is the script I wrote regarding it and I do not know what seems to be the fault. https://archive.ics.uci.edu/ml/datasets/Steel+Plates+Faults this is the dataset I run it on. I get multiple errors, one of them being "Classification metrics can’t handle a mix of multilabel-indicator and continuous-multioutput targets" import pandas as pd import numpy as ..

#### Category : random-forest

Trying to train a random forest classifier as below: %%time # defining model Model = RandomForestClassifier(random_state=1) # Parameter grid to pass in RandomSearchCV param_grid = { "n_estimators": [200,250,300], "min_samples_leaf": np.arange(1, 4), "max_features": [np.arange(0.3, 0.6, 0.1),"sqrt"], "max_samples": np.arange(0.4, 0.7, 0.1) } #Calling RandomizedSearchCV randomized_cv = RandomizedSearchCV(estimator=Model, param_distributions=param_grid, n_iter=50, n_jobs = -1, scoring=scorer, cv=5, random_state=1) #Fitting parameters ..

I currently have to make a program to predict pt scores based on people’s EEGs. Most of the models I tried don’t work well. I have less than 50 samples to work with, each with 17 data points. And 6 classes to sort into. Despite my best efforts, I can’t find a model that can ..

I’m trying to calculate the Cross_Validation_Score for the unseen data: y = df1[‘label’].astype(int) X = df1.drop(‘label’, axis=1) column_trans = make_column_transformer((OneHotEncoder(handle_unknown=’ignore’),[‘region’]),remainder=’passthrough’) from sklearn.pipeline import make_pipeline pipe = make_pipeline(column_trans, RandomForestRegressor(n_estimators=300, random_state=0)) pipe.fit(X, y) preds = pipe.predict(df2) cross_val_scores_r2 = cross_val_score(pipe,X,y,cv=5) Now as you can see, I have trained the model already and the prediction part preds is working ..

I am trying to implement leave one out cross-validation to evaluate my algorithm. I am using the UCI HCV dataset(https://archive.ics.uci.edu/ml/datasets/HCV+data) I implemented the below code to my data: X = df.drop("Category", axis=1) X.head() # y data y = df["Category"] y.head() cv = LeaveOneOut() y_true, y_pred = list(), list() for train_index, test_index in cv.split(X): #print("TRAIN:", train_index, ..

I want to use RandomForestClassifier for sentiment classification. The x contains data in string text, so I used LabelEncoder to convert strings. Y contains data in numbers. And my code is this: import pandas as pd import numpy as np from sklearn.model_selection import * from sklearn.ensemble import * from sklearn import * from sklearn.preprocessing.label import ..

The starting position is the following: There are categories 1 and 2, as well as features A, B and C. A representation would look like this: A B C 1 2 100 75 10 2.5 200 100 150 20 1 150 50 40 3 4 420 80 35 0 3.2 700 170 0 1 0.4 ..

I am learning ML and DL algorithms. When proceeding with my book I’ve stumbled onto RandomForest algorithm, I wanted to try it with forecasting function ‘sequence’ and it’s derivative. The f_data structure looks like this: f_value f_derivative_val 0 0 0 1 3 5 2 14 17 …. The code for random forest is not mine, ..

I am doing a machine learning project. I try to predict a continuous value based on 6 features(continuous and normalized). I have checked the multicollinearity and there is none between the 6 selected features. I built an rfr model and an ExtraTreesRegressor. I separated my data into X_train, y_train, X_test, y_test. When I run the ..

when I Use the RandomForestClassifier along with the GridSearchCV tool, it shows the following error. ValueError: Invalid parameter learning_rate for estimator RandomForestClassifier(random_state=12). Check the list of available parameters with estimator.get_params().keys(). # Use the RandomForestClassifier along with the GridSearchCV tool. Run the GridSearchCV using the following: from sklearn import svm, datasets from sklearn.model_selection import GridSearchCV rfc=RandomForestClassifier(random_state=12) ..

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