训练集和测试集的随机森林回归精度不同[闭合]

我对机器学习和Python都是新手。我试图在UCI存储库中的一个数据集上构建一个随机森林回归模型。这是我的第一个ML模型。我的方法可能是完全错误的。

https://archive.ics.uci.edu/ml/datasets/abalone

下面是我编写的全部工作代码。我在Windows 7 x64操作系统上使用Python 3.6.4(请原谅我的代码太长)。

import tkinter as tk # Required for enabling GUI options
from tkinter import messagebox # Required for pop-up window
from tkinter import filedialog # Required for getting full path of file
import pandas as pd # Required for data handling
from sklearn.model_selection import train_test_split # Required for splitting data into training and test set
from sklearn.ensemble import RandomForestRegressor # Required to build random forest

#------------------------------------------------------------------------------------------------------------------------#
# Create an instance of tkinter and hide the window

root = tk.Tk() # Create an instance of tkinter
root.withdraw() # Hides root window
#root.lift() # Required for pop-up window management
root.attributes("-topmost", True) # To make pop-up window stay on top of all other windows

#------------------------------------------------------------------------------------------------------------------------#
# This block of code reads input file using tkinter GUI options

print("Reading input file...")

# Pop up window to ask user the input file
File_Checker = messagebox.askokcancel("Random Forest Regression Prompt",
                                      "At The Prompt, Enter 'Abalone_Data.csv' File.")

# Kill the execution if user selects "Cancel" in the above pop-up window
if (File_Checker == False):
    quit()
else:
    del(File_Checker)

file_loop = 0

while (file_loop == 0):
    # Get path of base file
    file_path =  filedialog.askopenfilename(initialdir = "/",
                                            title = "File Selection Prompt",
                                            filetypes = (("XLSX Files","*.*"), ))

    # Condition to check if user selected a file or not
    if (len(file_path) < 1):
        # Pop-up window to warn uer that no file was selected
        result = messagebox.askretrycancel("File Selection Prompt Error",
                                           "No file has been selected. \nWhat do you want to do?")

        # Condition to repeat the loop or quit program execution
        if (result == True):
            continue
        else:
            quit()

    # Get file name
    file_name = file_path.split("/") # Splits the file with "/" as the delimiter and returns a list
    file_name = file_name[-1] # extracts the last element of the list

    # Condition to check if correct file was selected or not
    if (file_name != "Abalone_Data.csv"):
        result = messagebox.askretrycancel("File Selection Prompt Error",
                                           "Incorrect file selected. \nWhat do you want to do?")

        # Condition to repeat the loop or quit program execution
        if (result == True):
            continue
        else:
            quit()

    # Read the base file
    input_file = pd.read_csv(file_path,
                             sep = ',',
                             encoding = 'utf-8',
                             low_memory = True)

    break

# Delete unwanted files
del(file_loop, file_name)

#------------------------------------------------------------------------------------------------------------------------#
print("Preparing dependent and independent variables...")

# Create Separate dataframe consisting of only dependent variable
y = pd.DataFrame(input_file['Rings'])

# Create Separate dataframe consisting of only independent variable
X = input_file.drop(columns = ['Rings'], inplace = False, axis = 1)

#------------------------------------------------------------------------------------------------------------------------#
print("Handling Dummy Variable Trap...")

# Create a new dataframe to handle categorical data
# This method splits the dategorical data column into separate columns
# This is to ensure we get rid of the dummy variable trap
dummy_Sex = pd.get_dummies(X['Sex'], prefix = 'Sex', prefix_sep = '_', drop_first = True)

# Remove the speciic columns from the dataframe
# These are the categorical data columns which split into separae columns in the previous step
X.drop(columns = ['Sex'], inplace = True, axis = 1)

# Merge the new columns to the original dataframe
X = pd.concat([X, dummy_sex], axis = 1)

#------------------------------------------------------------------------------------------------------------------------#
y = y.values 
X = X.values

#------------------------------------------------------------------------------------------------------------------------#
print("Splitting datasets to training and test sets...")

# Splitting the data into training set and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

#------------------------------------------------------------------------------------------------------------------------#
print("Fitting Random Forest regression on training set")

# Fitting the regression model to the dataset
regressor = RandomForestRegressor(n_estimators = 100, random_state = 50)
regressor.fit(X_train, y_train.ravel()) # Using ravel() to avoid getting 'DataConversionWarning' warning message

#------------------------------------------------------------------------------------------------------------------------#
print("Predicting Values")

# Predicting a new result with regression
y_pred = regressor.predict(X_test)

# Enter values for new prediction as a Dictionary
test_values = {'Sex_I' : 0,
               'Sex_M' : 0,
               'Length' : 0.5,
               'Diameter' : 0.35,
               'Height' : 0.8,
               'Whole_Weight' : 0.223,
               'Shucked_Weight' : 0.09,
               'Viscera_Weight' : 0.05,
               'Shell_Weight' : 0.07}

# Convert dictionary into dataframe
test_values = pd.DataFrame(test_values, index = [0])

# Rearranging columns as required
test_values = test_values[['Length','Diameter','Height','Whole_Weight','Shucked_Weight','Viscera_Weight',
                           'Viscera_Weight', 'Sex_I', 'Sex_M']]

# Applying feature scaling
#test_values = sc_X.transform(test_values)

# Predicting values of new data
new_pred = regressor.predict(test_values)

#------------------------------------------------------------------------------------------------------------------------#
"""
print("Building Confusion Matrix...")

# Making the confusion matrix
cm = confusion_matrix(y_test, y_pred)
"""
#------------------------------------------------------------------------------------------------------------------------#
print("\n")
print("Getting Model Accuracy...")

# Get regression details
#print("Estimated Coefficient = ", regressor.coef_)
#print("Estimated Intercept = ", regressor.intercept_)
print("Training Accuracy = ", regressor.score(X_train, y_train))
print("Test Accuracy = ", regressor.score(X_test, y_test))

print("\n")
print("Printing predicted result...")
print("Result_of_Treatment = ", new_pred)

当我查看模型的准确性时,下面是我得到的。

Getting Model Accuracy...
Training Accuracy =  0.9359702279804791
Test Accuracy =  0.5695080680053354

下面是我的问题。 1) 为什么 Training Accuracy 和 Test Accuracy 那么远?

2) 如何知道此型号是否安装过度/不足?

3) 随机森林回归是正确的模型吗?如果没有,我如何为这个用例确定正确的模型?

3) 如何使用我创建的变量构建混淆矩阵?

4) 如何验证模型的性能?

我正在寻求您的指导,以便我也能从错误中吸取教训,提高我的建模技能。