问题
I'm working on a big data project for my school project. My dataset looks like this: https://github.com/gindeleo/climate/blob/master/GlobalTemperatures.csv
I'm trying to predict the next values of "LandAverageTemperature".
First, I've imported the csv into pandas and made it DataFrame named "df1".
After taking errors on my first tries in sklearn, I converted the "dt" column into datetime64 from string then added a column named "year" that shows only the years in the date values.-Its probably wrong-
df1["year"] = pd.DatetimeIndex(df1['dt']).year
After all of that, I prepared my data for reggression and called RandomForestReggressor:
landAvg = df1[["LandAverageTemperature"]]
year = df1[["year"]]
from sklearn.ensemble import RandomForestRegressor
rf_reg=RandomForestRegressor(n_estimators=10,random_state=0)
rf_reg.fit(year,landAvg.values.ravel())
print("Random forest:",rf_reg.predict(landAvg))
I ran the code and I've seen this result:
Random forest: [9.26558115 9.26558115 9.26558115 ... 9.26558115 9.26558115 9.26558115]
I'm not getting any errors but I don't think the results are correct -results are all the same as you can see-. Besides, when I want to get next 10 year's predictions, I don't know how to do that. I just get 1 result with this code. Can you help me for improve my code and get the right results? Thanks in advance for your help.
回答1:
It's not enought to use only year to predict temperature. Your need to use month data too. Here is a working example for starters:
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
df = pd.read_csv('https://raw.githubusercontent.com/gindeleo/climate/master/GlobalTemperatures.csv', usecols=['dt','LandAverageTemperature'], parse_dates=['dt'])
df = df.dropna()
df["year"] = df['dt'].dt.year
df["month"] = df['dt'].dt.month
X = df[["month", "year"]]
y = df["LandAverageTemperature"]
rf_reg=RandomForestRegressor(n_estimators=10,random_state=0)
rf_reg.fit(X, y)
y_pred = rf_reg.predict(X)
df_result = pd.DataFrame({'year': X['year'], 'month': X['month'], 'true': y, 'pred': y_pred})
print('True values and predictions')
print(df_result)
print('Feature importances', list(zip(X.columns, rf_reg.feature_importances_)))
And here is output:
True values and predictions
year month true pred
0 1750 1 3.034 2.2944
1 1750 2 3.083 2.4222
2 1750 3 5.626 5.6434
3 1750 4 8.490 8.3419
4 1750 5 11.573 11.7569
... ... ... ... ...
3187 2015 8 14.755 14.8004
3188 2015 9 12.999 13.0392
3189 2015 10 10.801 10.7068
3190 2015 11 7.433 7.1173
3191 2015 12 5.518 5.1634
[3180 rows x 4 columns]
Feature importances [('month', 0.9543059863177156), ('year', 0.045694013682284394)]
来源：https://stackoverflow.com/questions/59460378/how-to-predict-correctly-in-sklearn-randomforestregressor