How to go about making available the clf.best_params_ after carrying a pipeline? For the code I have below, I get an: AttributeError: ‘GridSearchCV’ object has no attribute ‘best_params_’ Here is my code: Answer Your clf is never fitted. You probably meant clf.fit(X_train,y_train). Also, np.linspace(10,50,11) yields floats, while max_depth expects ints, so this may fail and you should probably add a
Tag: random-forest
Suspect overfitting binary classification toy problem with scikit-learn RandomForestClassifier
I’m trying to train a Random Forest to classify the species of a set of flowers from the iris dataset. However, the validation looks kind of weird to me, since it looks like the results are perfect, which is something I would not expect. Since I would like to perform a binary classification, I exclude from the training dataset the
Do Machine Learning Algorithms read data top-down or bottom up?
I’m new to Machine Learning and I’m a bit confused about how data is being read for the training/testing process. Assuming my data works with date and I want the model to read the later dates first before getting to the newer dates, the data is saved in the form of earliest date on line 1 and line n has
Outlier removal Isolation Forest
I’ve been trying to remove outliers from my database using isolation forest, but I can’t figure out how. I’ve seen the examples for credit card fraud and Salary but I can’t figure out how to apply them on each column as my database consists of 3862900 rows and 19 columns. I’ve uploaded an image of the head of my database.
Random Forest tuning with RandomizedSearchCV
I have a few questions concerning Randomized grid search in a Random Forest Regression Model. My parameter grid looks like this: and my code for the RandomizedSearchCV like this: is there any way to calculate the Root mean square at each parameter set? This would be more interesting to me as the R^2 score? If I now want to get
pipeline for RandomOversampler, RandomForestClassifier & GridSearchCV
I am working on a binary text classification problem. As the classes are highly imbalanced, I am using sampling techniques like RandomOversampler(). Then for classification I would use RandomForestClassifier() whose parameters need to be tuned using GridSearchCV(). I am trying to create a pipeline to do these in order but failed so far. It throws invalid parameters. Answer The parameters
What does the value of ‘leaf’ in the following xgboost model tree diagram means?
I am guessing that it is conditional probability given that the above (tree branch) condition exists. However, I am not clear on it. If you want to read more about the data used or how do we get this diagram then go to : http://machinelearningmastery.com/visualize-gradient-boosting-decision-trees-xgboost-python/ Answer Attribute leaf is the predicted value. In other words, if the evaluation of a
Retrieve list of training features names from classifier
Is there a way to retrieve the list of feature names used for training of a classifier, once it has been trained with the fit method? I would like to get this information before applying to unseen data. The data used for training is a pandas DataFrame and in my case, the classifier is a RandomForestClassifier. Answer Based on the
RandomForestClassifier import
I’ve installed Anaconda Python distribution with scikit-learn. While importing RandomForestClassifier: from sklearn.ensemble import RandomForestClassifier I have the following error: File “C:Anacondalibsite-packagessklearntreetree.py”, line 36, in <module> from . import _tree ImportError: cannot import name _tree What the problem can be there? Answer The problem was that I had the 64bit version of Anaconda and the 32bit sklearn.