SciKit-Learn Decision Tree Overfitting
$begingroup$
I'm pursuing a computer science minor at my university, and one class I'm in is Machine Learning.
We have a project to utilize a few algorithms we have learned so far.
I've been using SciKit-Learn to perform these algorithms, but when it comes to decision trees I keep getting a feeling I am overfitting.
I'm using a dataset about weather, giving characteristics such as city, state, month, year, wind direction, wind speed, etc... where the target variable is the average temperature for the day. Now I know this is hard to classify, as it is pretty much a continuous variable space, but I've simplified it to the predicted being within a range of 5 to the actual.
Here is a link to the csv I'm using
The following is my code:
address2 = 'C:/.../weather.csv'
weather = pd.read_csv(address2)
cityCode= le.fit_transform(weather.iloc[:,2])
windDirection = le.fit_transform(weather.iloc[:,3])
month = le.fit_transform(weather.iloc[:,8])
precip = le.fit_transform(weather.iloc[:,9])
windSpeed = le.fit_transform(weather.iloc[:,10])
state = le.fit_transform(weather.iloc[:,11])
week = le.fit_transform(weather.iloc[:,12])
year = le.fit_transform(weather.iloc[:,13])
Xweather = list(zip(cityCode,windDirection,month,precip,windSpeed,state,week,year))
yweather= weather.iloc[:,0]
yweather_test = train_test_split(Xweather, y, test_size = 0.2, random_state=413)
cWeather = tree.DecisionTreeClassifier()
cWeather.fit(Xweather_train,yweather_train)
accu_train_weather=np.sum(abs(cWeather.predict(Xweather_train)-yweather_train)<=5)/float(yweather_train.size)*100
accu_test_weather=np.sum(abs(cWeather.predict(Xweather_test)-yweather_test)<=5)/float(yweather_test.size)*100
print("Classificaton accuracy on training set", accu_train_weather, "%")
print("Classificaton accuracy on test set", accu_test_weather, "%")
My training set constantly gets 100% training accuracy, but the test set is constantly 57% accurate, which leads me to believe the tree is overfitting to the training set.
I know I'm not doing any pruning, but even when I do, I can get the same test accuracy as unpruned at best.
By pruning I mean setting the tree classifier to have a maximum number of leaves, minimum samples per leaf, and maximum depth.
I'm not an advanced coder (as you can probably tell by my code), but any help would be great.
machine-learning python scikit-learn decision-trees overfitting
asked 2 hours ago
PaulfryyPaulfryy
61
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Paulfryy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I'm pursuing a computer science minor at my university, and one class I'm in is Machine Learning.
We have a project to utilize a few algorithms we have learned so far.
I've been using SciKit-Learn to perform these algorithms, but when it comes to decision trees I keep getting a feeling I am overfitting.
I'm using a dataset about weather, giving characteristics such as city, state, month, year, wind direction, wind speed, etc... where the target variable is the average temperature for the day. Now I know this is hard to classify, as it is pretty much a continuous variable space, but I've simplified it to the predicted being within a range of 5 to the actual.
Here is a link to the csv I'm using
The following is my code:
address2 = 'C:/.../weather.csv'
weather = pd.read_csv(address2)
cityCode= le.fit_transform(weather.iloc[:,2])
windDirection = le.fit_transform(weather.iloc[:,3])
month = le.fit_transform(weather.iloc[:,8])
precip = le.fit_transform(weather.iloc[:,9])
windSpeed = le.fit_transform(weather.iloc[:,10])
state = le.fit_transform(weather.iloc[:,11])
week = le.fit_transform(weather.iloc[:,12])
year = le.fit_transform(weather.iloc[:,13])
Xweather = list(zip(cityCode,windDirection,month,precip,windSpeed,state,week,year))
yweather= weather.iloc[:,0]
yweather_test = train_test_split(Xweather, y, test_size = 0.2, random_state=413)
cWeather = tree.DecisionTreeClassifier()
cWeather.fit(Xweather_train,yweather_train)
accu_train_weather=np.sum(abs(cWeather.predict(Xweather_train)-yweather_train)<=5)/float(yweather_train.size)*100
accu_test_weather=np.sum(abs(cWeather.predict(Xweather_test)-yweather_test)<=5)/float(yweather_test.size)*100
print("Classificaton accuracy on training set", accu_train_weather, "%")
print("Classificaton accuracy on test set", accu_test_weather, "%")
My training set constantly gets 100% training accuracy, but the test set is constantly 57% accurate, which leads me to believe the tree is overfitting to the training set.
I know I'm not doing any pruning, but even when I do, I can get the same test accuracy as unpruned at best.
By pruning I mean setting the tree classifier to have a maximum number of leaves, minimum samples per leaf, and maximum depth.
I'm not an advanced coder (as you can probably tell by my code), but any help would be great.
machine-learning python scikit-learn decision-trees overfitting
asked 2 hours ago
PaulfryyPaulfryy
61
New contributor
Paulfryy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I'm pursuing a computer science minor at my university, and one class I'm in is Machine Learning.
We have a project to utilize a few algorithms we have learned so far.
I've been using SciKit-Learn to perform these algorithms, but when it comes to decision trees I keep getting a feeling I am overfitting.
I'm using a dataset about weather, giving characteristics such as city, state, month, year, wind direction, wind speed, etc... where the target variable is the average temperature for the day. Now I know this is hard to classify, as it is pretty much a continuous variable space, but I've simplified it to the predicted being within a range of 5 to the actual.
Here is a link to the csv I'm using
The following is my code:
address2 = 'C:/.../weather.csv'
weather = pd.read_csv(address2)
cityCode= le.fit_transform(weather.iloc[:,2])
windDirection = le.fit_transform(weather.iloc[:,3])
month = le.fit_transform(weather.iloc[:,8])
precip = le.fit_transform(weather.iloc[:,9])
windSpeed = le.fit_transform(weather.iloc[:,10])
state = le.fit_transform(weather.iloc[:,11])
week = le.fit_transform(weather.iloc[:,12])
year = le.fit_transform(weather.iloc[:,13])
Xweather = list(zip(cityCode,windDirection,month,precip,windSpeed,state,week,year))
yweather= weather.iloc[:,0]
yweather_test = train_test_split(Xweather, y, test_size = 0.2, random_state=413)
cWeather = tree.DecisionTreeClassifier()
cWeather.fit(Xweather_train,yweather_train)
accu_train_weather=np.sum(abs(cWeather.predict(Xweather_train)-yweather_train)<=5)/float(yweather_train.size)*100
accu_test_weather=np.sum(abs(cWeather.predict(Xweather_test)-yweather_test)<=5)/float(yweather_test.size)*100
print("Classificaton accuracy on training set", accu_train_weather, "%")
print("Classificaton accuracy on test set", accu_test_weather, "%")
My training set constantly gets 100% training accuracy, but the test set is constantly 57% accurate, which leads me to believe the tree is overfitting to the training set.
I know I'm not doing any pruning, but even when I do, I can get the same test accuracy as unpruned at best.
By pruning I mean setting the tree classifier to have a maximum number of leaves, minimum samples per leaf, and maximum depth.
I'm not an advanced coder (as you can probably tell by my code), but any help would be great.
machine-learning python scikit-learn decision-trees overfitting
asked 2 hours ago
PaulfryyPaulfryy
61
New contributor
Paulfryy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm pursuing a computer science minor at my university, and one class I'm in is Machine Learning.
We have a project to utilize a few algorithms we have learned so far.
I've been using SciKit-Learn to perform these algorithms, but when it comes to decision trees I keep getting a feeling I am overfitting.
I'm using a dataset about weather, giving characteristics such as city, state, month, year, wind direction, wind speed, etc... where the target variable is the average temperature for the day. Now I know this is hard to classify, as it is pretty much a continuous variable space, but I've simplified it to the predicted being within a range of 5 to the actual.
Here is a link to the csv I'm using
The following is my code:
address2 = 'C:/.../weather.csv'
weather = pd.read_csv(address2)
cityCode= le.fit_transform(weather.iloc[:,2])
windDirection = le.fit_transform(weather.iloc[:,3])
month = le.fit_transform(weather.iloc[:,8])
precip = le.fit_transform(weather.iloc[:,9])
windSpeed = le.fit_transform(weather.iloc[:,10])
state = le.fit_transform(weather.iloc[:,11])
week = le.fit_transform(weather.iloc[:,12])
year = le.fit_transform(weather.iloc[:,13])
Xweather = list(zip(cityCode,windDirection,month,precip,windSpeed,state,week,year))
yweather= weather.iloc[:,0]
yweather_test = train_test_split(Xweather, y, test_size = 0.2, random_state=413)
cWeather = tree.DecisionTreeClassifier()
cWeather.fit(Xweather_train,yweather_train)
accu_train_weather=np.sum(abs(cWeather.predict(Xweather_train)-yweather_train)<=5)/float(yweather_train.size)*100
accu_test_weather=np.sum(abs(cWeather.predict(Xweather_test)-yweather_test)<=5)/float(yweather_test.size)*100
print("Classificaton accuracy on training set", accu_train_weather, "%")
print("Classificaton accuracy on test set", accu_test_weather, "%")
My training set constantly gets 100% training accuracy, but the test set is constantly 57% accurate, which leads me to believe the tree is overfitting to the training set.
I know I'm not doing any pruning, but even when I do, I can get the same test accuracy as unpruned at best.
By pruning I mean setting the tree classifier to have a maximum number of leaves, minimum samples per leaf, and maximum depth.
I'm not an advanced coder (as you can probably tell by my code), but any help would be great.
machine-learning python scikit-learn decision-trees overfitting
machine-learning python scikit-learn decision-trees overfitting
asked 2 hours ago
PaulfryyPaulfryy
61
New contributor
Paulfryy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 hours ago
PaulfryyPaulfryy
61
New contributor
Paulfryy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 hours ago
PaulfryyPaulfryy
61
New contributor
Paulfryy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 hours ago
PaulfryyPaulfryy
61
asked 2 hours ago
PaulfryyPaulfryy
61
61
New contributor
Paulfryy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Paulfryy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Paulfryy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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