Understanding Learning Curve
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I am using the sklearn learning curve method. From what I understand the learning curve will show me the ideal split of my data into training and testing data. What is the optimum amount of training examples needed, how does this number reflect the proportion between training and testing split?
I am basically having a tough time trying to analyse what it means and how it can help me in building a more accurate classifier.
The parameters passed are the x which is the features and y is which class it belongs to 1 or 0.
cv = ShuffleSplit(n_splits=10, test_size=0.5, random_state=0)
plot_learning_curve(MLPClassifier(), "Learning Curves", x, y, cv=cv, n_jobs=-1)
python scikit-learn unsupervised-learning
asked 4 mins ago
OmanOman
81
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add a comment |
$begingroup$
I am using the sklearn learning curve method. From what I understand the learning curve will show me the ideal split of my data into training and testing data. What is the optimum amount of training examples needed, how does this number reflect the proportion between training and testing split?
I am basically having a tough time trying to analyse what it means and how it can help me in building a more accurate classifier.
The parameters passed are the x which is the features and y is which class it belongs to 1 or 0.
cv = ShuffleSplit(n_splits=10, test_size=0.5, random_state=0)
plot_learning_curve(MLPClassifier(), "Learning Curves", x, y, cv=cv, n_jobs=-1)
python scikit-learn unsupervised-learning
asked 4 mins ago
OmanOman
81
$endgroup$
add a comment |
$begingroup$
I am using the sklearn learning curve method. From what I understand the learning curve will show me the ideal split of my data into training and testing data. What is the optimum amount of training examples needed, how does this number reflect the proportion between training and testing split?
I am basically having a tough time trying to analyse what it means and how it can help me in building a more accurate classifier.
The parameters passed are the x which is the features and y is which class it belongs to 1 or 0.
cv = ShuffleSplit(n_splits=10, test_size=0.5, random_state=0)
plot_learning_curve(MLPClassifier(), "Learning Curves", x, y, cv=cv, n_jobs=-1)
python scikit-learn unsupervised-learning
asked 4 mins ago
OmanOman
81
$endgroup$
I am using the sklearn learning curve method. From what I understand the learning curve will show me the ideal split of my data into training and testing data. What is the optimum amount of training examples needed, how does this number reflect the proportion between training and testing split?
I am basically having a tough time trying to analyse what it means and how it can help me in building a more accurate classifier.
The parameters passed are the x which is the features and y is which class it belongs to 1 or 0.
cv = ShuffleSplit(n_splits=10, test_size=0.5, random_state=0)
plot_learning_curve(MLPClassifier(), "Learning Curves", x, y, cv=cv, n_jobs=-1)
python scikit-learn unsupervised-learning
python scikit-learn unsupervised-learning
asked 4 mins ago
OmanOman
81
asked 4 mins ago
OmanOman
81
asked 4 mins ago
OmanOman
81
asked 4 mins ago
OmanOman
81
asked 4 mins ago
OmanOman
81
81
add a comment |
add a comment |
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