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 the 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.