[英]Why does Random Forest Regression predict the exact same value?
I am attempting to use Scikit-Learn's Random Forest regressor to predict Nominal GDP from Real GDP.我正在尝试使用 Scikit-Learn 的随机森林回归器从实际 GDP 中预测名义 GDP。
I read the data from a webstite and clean it up a bit, then synthesize a dataframe with what I have forecasted are the next three years of Real GDP.我从网站上读取数据并稍微清理一下,然后将 dataframe 与我预测的未来三年的实际 GDP 综合起来。
I have the following code:我有以下代码:
from sklearn.ensemble import RandomForestRegressor
gdp = pd.read_html('https://www.thebalance.com/us-gdp-by-year-3305543')[0]
gdp.columns = gdp.iloc[0]
gdp = gdp[1:]
gdp['Year'] = gdp['Year'].astype(int)
gdp['Nominal GDP (trillions)'] = gdp['Nominal GDP (trillions)'].str.replace(',', '.').str.replace('$', '').astype(float)
gdp['Real GDP (trillions)'] = gdp['Real GDP (trillions)'].str.replace(',', '.').str.replace('$', '').astype(float)
X = pd.DataFrame(gdp['Real GDP (trillions)'].copy())
y = pd.DataFrame(gdp['Nominal GDP (trillions)'].copy())
X_pred = pd.DataFrame(data = [18.313, 18.960, 19.643], columns = ['Real GDP (trillions)'])
reg = RandomForestRegressor(n_estimators = 300)
reg.fit(X, y.values.ravel())
y_pred = reg.predict(X_pred)
It returns the following prediction: 1 |它返回以下预测:1 | 2 |
2 | 3 ---|---|--- 19.72172 |
3 ---|---|--- 19.72172 | 21.05464667 |
21.05464667 | 21.05464667
21.05464667
Why are the second and third predictions identical?为什么第二个和第三个预测相同? It happens even if I change the X_pred values to something like
[18.313, 18.960, 39.643]
即使我将 X_pred 值更改为
[18.313, 18.960, 39.643]
之类的值,也会发生这种情况
In your training data, there's only one value > 18.960:在您的训练数据中,只有一个值 > 18.960:
X[X.values>18.960]
Real GDP (trillions)
91 19.092
So it is highly unlikely you will end up with a value that can split 18.960 and 19.643, or for that matter, 18.960 and 39.643.因此,您极不可能最终得到一个可以拆分 18.960 和 19.643 的值,或者就此而言,18.960 和 39.643。 It is not linear regression where you can interpolate.
它不是可以插值的线性回归。
We can check the thresholds for each tree:我们可以检查每棵树的阈值:
thres = np.unique([j for i in reg.estimators_ for j in i.tree_.threshold])
np.sort(thres)[-10:]
array([17.80000019, 17.9375 , 18.00199986, 18.05999947, 18.20950031,
18.26199913, 18.41149998, 18.41599941, 18.61799908, 18.88999939])
The largest value of your threshold is not able to split the 2 values you are trying to predict, hence they will always end up in the same nodes, giving you the same prediction.您的阈值的最大值无法拆分您尝试预测的 2 个值,因此它们将始终位于相同的节点中,从而为您提供相同的预测。
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