Develop Reference

How can i combine xgboost with adaboost?

I have combined random forest with adaboost as
clf = AdaBoostClassifier(n_estimators=10, base_estimator=RandomForestClassifier(n_estimators=10,max_depth=20))
now i want to combine adaboost with xgboost and i have tried like this:
from sklearn.ensemble import AdaBoostClassifier
from xgboost import XGBClassifier
clf = AdaBoostClassifier(base_estimator=XGBClassifier(eval_metric='mlogloss'))
and it is not working correctly. How to do this?

use would just use it like this
import lib1, lib2, lib3, lib4, lib5

Import Error: cannot import name 'tree' from 'sklearn.tree'

I am on my second day of re-taking Python for the gazillionth time!
I am doing a tutorial on ML in Python, using the following code:
import sklearn.tree
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import tree
music_data = pd.read_csv('music.csv')
x = music_data.drop(columns=['genre'])
y = music_data['genre']
model = DecisionTreeClassifier()
model.fit(x,y)
tree.export_graphviz(model, out_file='music-recommender.dot',
feature_names=['age','gender'],
class_names= sorted(y.unique()),
label='all',
rounded=True,
filled=True)
I keep getting the following error:
ImportError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_13088/3820271611.py in <module>
2 import pandas as pd
3 from sklearn.tree import DecisionTreeClassifier
----> 4 from sklearn.tree import tree
5
6 music_data = pd.read_csv('music.csv')
ImportError: cannot import name 'tree' from 'sklearn.tree' (C:\Anaconda\lib\site-packages\sklearn\tree\__init__.py)
I've tried to find a solution online, but I don't think it's the version of Python/Anaconda because I literally just installed both. I also don't think it's the sklearn.tree since I was able to import DecisionClassifer.

As this answer indicates, you're looking at some older code; this is always a risk with programming. But there's another thing you need to know about your code.
First off, scikit-learn contains several modules, and almost everything you need from it is in one of those. In my experience, most people import things like this:
from sklearn.tree import DecisionTreeRegressor # A regressor class.
from sklearn.tree import plot_tree # A helpful function.
from sklearn.metrics import mean_squared_error # An evaluation function.
It looks like the tutorial wants something similar to plot_tree(). This new-ish function is much easier to use than the older Graphviz visualization. So unless you really need the DOT file for some reasons, you should be able to do this:
from sklearn.tree import plot_tree
sklearn.tree.plot_tree(model)
Bottom line: there will probably be more broken things in that material. So if I were you I'd either make a new environment with a version of sklearn matching whatever material you're using... or ditch that material and look for something newer.

from sklearn.tree import tree looks wrong. Did you mean from sklearn import tree ?

According to the official Scikit Learn Decision Trees Documentation you really do not need too much of importing.
It can be done simply as follows:
from sklearn import tree
import pandas as pd
music_data = pd.read_csv('music.csv')
X = music_data.drop(columns=['genre'])
y = music_data['genre']
model = tree.DecisionTreeClassifier()
model.fit(X,y)

When I try to use .fit on my decision tree classifier it gives an error in notebook

This is my code.
I use jupyter notebook trying to start machine learning after learning the basics so if you have other tips much appreciated.
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
music_data = pd.read_csv('music.csv')
X = music_data.drop(columns = ['genre'])
y = music_data['genre']
model = DecisionTreeClassifier
model.fit(X,y)
music_data
and I get an error "model.fit is missing the positional argument y".

MLPRegressor problem with attribute loss_curve_

I want to plot the loss_curve by using the following code:
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor
def plotCurves(Xtrain,ytrain,Xval,yval):
solver=["lbfgs", "sgd", "adam"]
for i in solver:
mlp=MLPRegressor(activation='relu',max_iter=1000,solver=i)
mlp.fit(Xtrain,ytrain)
pred=mlp.predict(Xval)
print (mlp.score(Xval,yval))
pd.DataFrame(mlp.loss_curve_).plot()
However, when I run my code the following error appears:
'MLPRegressor' object has no attribute 'loss_curve_'
and in the Anaconda IDE version 1.9.7 it appears this method when I am coding.
What can I try to solve this?

Only the stochastic solvers will expose a loss_curve_ attribute on the estimator after fit, so in your first iteration it fails with the lbfgs solver. You can verify this with the following:
from sklearn.datasets import make_classification
from sklearn.neural_network import MLPRegressor
​
X, y = make_classification(n_samples=5)
​
solver=[
"lbfgs",
"sgd",
"adam"
]
​
for i in solver:
mlp = MLPRegressor(activation='relu',solver=i)
mlp.fit(X,y)
print(hasattr(mlp, "loss_curve_"))
False
True
True
If you want to access this attribute, you'll want to stick with either the adam or sgd solver.

Issues printing the class, name and accuracy score of the voting classifier

I would like to train a voiting classifer in SciKit-Learn with three different classifiers. I'm having issues with the final step, which is printing the final accuracy scores of the classifiers.
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
import pandas as pd
import numpy as np
log_clf=LogisticRegression()
rnd_clf=RandomForestClassifier()
svm_clf=SVC()
voting_clf=VotingClassifier(estimators=[('lr',log_clf),('rf',rnd_clf),('svc',svm_clf)],voting='hard')
voting_clf.fit(X_train, y_train)
I am getting errors when I run the following code:
for clf in (log_clf, rnd_clf, svm_clf, voting_clf):
clf.fit(X_train, y_train)
y_predict=clf.predict(X_test)
print(clf._class_._name_,accuracy_score(y_test,y_pred))
When I run this chunk of code I get the following:
AttributeError: 'LogisticRegression' object has no attribute '_class_'
I am assuming that calling 'class'is a bit outdated, so I changed class to 'classes_':
for clf in (log_clf, rnd_clf, svm_clf, voting_clf):
clf.fit(X_train, y_train)
y_pred=clf.predict(X_test)
print(clf.classes_._name_,accuracy_score(y_test,y_pred))
When I run this chunk of code I get the following:
AttributeError: 'numpy.ndarray' object has no attribute '_name_'
When I remove 'name' and run the following code, I still get an error:
for clf in (log_clf, rnd_clf, svm_clf, voting_clf):
clf.fit(X_train, y_train)
y_pred=clf.predict(X_test)
print(clf.classes_,accuracy_score(y_test,y_pred))
Error:
NameError: name 'accuracy_score' is not defined
I'm not sure why accuracy_score is not defined seeing that imported the library

For the first error about class, you need to have two underscores here.
Change
print(clf._class_._name_,accuracy_score(y_test,y_pred))
to:
print(clf.__class__.__name__, accuracy_score(y_test,y_pred))
See this question for other ways to get the name of an object in python:
Getting the class name of an instance?
Now for the second error about 'accuracy_score' not defined, this happens when you have not imported the accuracy_score correctly. But I can see that in your code you have imported the accuracy_score. So are you sure that you are executing the line print(clf.__class__.__name__, accuracy_score(y_test,y_pred)) in the same file? or in any different file?