Here is the dataset information [Paste Dataset information from Kaggle] Write me a machine learning code that predicts magnitude of earthquakes.
这里是代码。
但是你注意到了吗？它创建了自己的数据集，而不是使用我们上传的那个。
这没关系；大语言模型偶尔会出错，这就是它们仍然需要人类的原因。
这里是调整提示词的方法。
You should not create an example data, we already have the data that I sent to you an look from there and write code here accordingly
好了。如你所见，它使用了我们的数据集，但这次它自己选择了特征。我们需要对此进行更改。
这里是我使用的提示词。
you should select features based on the feature importance, maybe gini index?
现在，让我们向ChatGPT输入信息。
Here all features. [paste df.info codes output here- all column names] You should select all at first, and then label encoding then and select important ones with gini
在这里，我意识到我们使用了很多不同的方法来创建最佳模型，比如：
归一化
应用5种不同的机器学习模型
通过Gini Importance选择不同的模型
对数变换
降维
我让GPT 4o-Canvas为这些方法生成代码。这里是我最后的代码。
import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor from sklearn.linear_model import LinearRegression from sklearn.tree import DecisionTreeRegressor from sklearn.svm import SVR from sklearn.metrics import mean_squared_error, r2_score from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler from sklearn.decomposition import PCA import numpy as np import matplotlib.pyplot as plt # Load the dataset df = pd.read_csv('/mnt/data/earthquakes.csv') # Select all features initially all_features = df.columns.tolist() all_features.remove('magnitude')# Remove target variable from features # Handle categorical features using Label Encoding categorical_features = df.select_dtypes(include=['object']).columns label_encoders = {} for feature in categorical_features: le = LabelEncoder() df[feature] = le.fit_transform(df[feature].astype(str)) label_encoders[feature] = le # Drop rows with missing values in the selected features and target df = df.dropna(subset=all_features + ['magnitude']) # Define the input features (X) and the target variable (y) X = df[all_features] y = df['magnitude'] # Check for skewness and apply log transformation where necessary for col in X.columns: if X[col].skew() > 1 or X[col].skew() < -1: X.loc[:, col] = np.log1p(X[col]) # Normalize the features using StandardScaler scaler_standard = StandardScaler() X_standard = scaler_standard.fit_transform(X) # Normalize the features using MinMaxScaler scaler_minmax = MinMaxScaler() X_minmax = scaler_minmax.fit_transform(X) # Dimensionality Reduction using PCA (if number of features is high) pca_threshold = 0.95 if X_standard.shape[1] > 10: pca_standard = PCA(n_components=pca_threshold) X_standard = pca_standard.fit_transform(X_standard) pca_minmax = PCA(n_components=pca_threshold) X_minmax = pca_minmax.fit_transform(X_minmax) # Split the data into training and testing sets for both normalization methods X_train_standard, X_test_standard, y_train, y_test = train_test_split(X_standard, y, test_size=0.2, random_state=42) X_train_minmax, X_test_minmax, _, _ = train_test_split(X_minmax, y, test_size=0.2, random_state=42) # Initialize RandomForestRegressor to determine feature importance feature_importance_model = RandomForestRegressor(n_estimators=100, random_state=42) feature_importance_model.fit(X_train_standard, y_train) # Calculate feature importance using the features used in the training set after transformations actual_features = X.columns[:X_train_standard.shape[1]] feature_importances = feature_importance_model.feature_importances_ feature_importance_df = pd.DataFrame({'feature': actual_features, 'importance': feature_importances}) feature_importance_df = feature_importance_df.sort_values(by='importance', ascending=False) # Select the most important features (top 8 and top 10) top_8_features = feature_importance_df['feature'].head(8).tolist() top_10_features = feature_importance_df['feature'].head(10).tolist() # Define input features for top 8 and top 10 feature sets X_top_8 = df[top_8_features].copy() X_top_10 = df[top_10_features].copy() # Check for skewness in top features and apply log transformation where necessary for col in X_top_8.columns: if X_top_8[col].skew() > 1 or X_top_8[col].skew() < -1: X_top_8.loc[:, col] = np.log1p(X_top_8[col]) for col in X_top_10.columns: if X_top_10[col].skew() > 1 or X_top_10[col].skew() < -1: X_top_10.loc[:, col] = np.log1p(X_top_10[col]) # Normalize the selected top features using both scalers X_top_8_standard = scaler_standard.fit_transform(X_top_8) X_top_8_minmax = scaler_minmax.fit_transform(X_top_8) X_top_10_standard = scaler_standard.fit_transform(X_top_10) X_top_10_minmax = scaler_minmax.fit_transform(X_top_10) # Dimensionality Reduction using PCA for top 8 and top 10 features if X_top_8_standard.shape[1] > 5: pca_top_8_standard = PCA(n_components=pca_threshold) X_top_8_standard = pca_top_8_standard.fit_transform(X_top_8_standard) pca_top_8_minmax = PCA(n_components=pca_threshold) X_top_8_minmax = pca_top_8_minmax.fit_transform(X_top_8_minmax) if X_top_10_standard.shape[1] > 5: pca_top_10_standard = PCA(n_components=pca_threshold) X_top_10_standard = pca_top_10_standard.fit_transform(X_top_10_standard) pca_top_10_minmax = PCA(n_components=pca_threshold) X_top_10_minmax = pca_top_10_minmax.fit_transform(X_top_10_minmax) # Split the data for top 8 and top 10 feature sets X_train_8_standard, X_test_8_standard, _, _ = train_test_split(X_top_8_standard, y, test_size=0.2, random_state=42) X_train_8_minmax, X_test_8_minmax, _, _ = train_test_split(X_top_8_minmax, y, test_size=0.2, random_state=42) X_train_10_standard, X_test_10_standard, _, _ = train_test_split(X_top_10_standard, y, test_size=0.2, random_state=42) X_train_10_minmax, X_test_10_minmax, _, _ = train_test_split(X_top_10_minmax, y, test_size=0.2, random_state=42) # Define the models to be used models = { 'Random Forest': RandomForestRegressor(n_estimators=100, random_state=42), 'Gradient Boosting': GradientBoostingRegressor(n_estimators=100, random_state=42), 'Linear Regression': LinearRegression(), 'Decision Tree': DecisionTreeRegressor(random_state=42), 'Support Vector Regressor': SVR() } # Train each model and evaluate performance for each combination model_results = {} normalizations = [ ('StandardScaler - Top 8 Features', X_train_8_standard, X_test_8_standard), ('MinMaxScaler - Top 8 Features', X_train_8_minmax, X_test_8_minmax), ('StandardScaler - Top 10 Features', X_train_10_standard, X_test_10_standard), ('MinMaxScaler - Top 10 Features', X_train_10_minmax, X_test_10_minmax) ] for normalization_name, X_train, X_test in normalizations: for model_name, model in models.items(): model.fit(X_train, y_train) y_pred = model.predict(X_test) mse = mean_squared_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) model_results[f'{model_name} ({normalization_name})'] = {'MSE': mse, 'R2': r2} print(f"{model_name} ({normalization_name}) - Mean Squared Error: {mse}, R^2 Score: {r2}") # Plot the results model_names = list(model_results.keys()) mse_values = [model_results[name]['MSE'] for name in model_names] r2_values = [model_results[name]['R2'] for name in model_names] fig, ax = plt.subplots(1, 2, figsize=(20, 10)) # Bar plot for MSE ax[0].barh(model_names, mse_values, color='skyblue') ax[0].set_title('Mean Squared Error Comparison') ax[0].set_xlabel('MSE') # Bar plot for R2 Score ax[1].barh(model_names, r2_values, color='lightgreen') ax[1].set_title('R^2 Score Comparison') ax[1].set_xlabel('R^2 Score') plt.tight_layout() plt.show()
这里是输出的第一部分。
Random Forest (StandardScaler - Top 8 Features) - Mean Squared Error: 0.036684462750000014, R^2 Score: 0.8779777294037107 Gradient Boosting (StandardScaler - Top 8 Features) - Mean Squared Error: 0.03171284438291409, R^2 Score: 0.8945146531096477 Linear Regression (StandardScaler - Top 8 Features) - Mean Squared Error: 0.2030328908150269, R^2 Score: 0.3246586569411304 Decision Tree (StandardScaler - Top 8 Features) - Mean Squared Error: 0.014955, R^2 Score: 0.9502556962820041 Support Vector Regressor (StandardScaler - Top 8 Features) - Mean Squared Error: 0.19093007009631902, R^2 Score: 0.3649158545122343 Random Forest (MinMaxScaler - Top 8 Features) - Mean Squared Error: 0.0368944044999999, R^2 Score: 0.8772794073592385 Gradient Boosting (MinMaxScaler - Top 8 Features) - Mean Squared Error: 0.02585888543990656, R^2 Score: 0.9139864760192863 Linear Regression (MinMaxScaler - Top 8 Features) - Mean Squared Error: 0.2030123102574625, R^2 Score: 0.3247271133440839 Decision Tree (MinMaxScaler - Top 8 Features) - Mean Squared Error: 0.021312499999999988, R^2 Score: 0.9291089620200744 Support Vector Regressor (MinMaxScaler - Top 8 Features) - Mean Squared Error: 0.1900837276420734, R^2 Score: 0.3677310143981205 Random Forest (StandardScaler - Top 10 Features) - Mean Squared Error: 0.014616167999999801, R^2 Score: 0.9513827415456206 Gradient Boosting (StandardScaler - Top 10 Features) - Mean Squared Error: 0.005215391903357051, R^2 Score: 0.9826522207389522 Linear Regression (StandardScaler - Top 10 Features) - Mean Squared Error: 0.024659998640491166, R^2 Score: 0.9179742920723531 Decision Tree (StandardScaler - Top 10 Features) - Mean Squared Error: 0.01719999999999999, R^2 Score: 0.9427882297593093 Support Vector Regressor (StandardScaler - Top 10 Features) - Mean Squared Error: 0.012001988809648145, R^2 Score: 0.9600781961506436 Random Forest (MinMaxScaler - Top 10 Features) - Mean Squared Error: 0.013746897999999952, R^2 Score: 0.9542741645408019 Gradient Boosting (MinMaxScaler - Top 10 Features) - Mean Squared Error: 0.0066618489718651116, R^2 Score: 0.9778409201885739 Linear Regression (MinMaxScaler - Top 10 Features) - Mean Squared Error: 0.024620634208737134, R^2 Score: 0.918105228631956 Decision Tree (MinMaxScaler - Top 10 Features) - Mean Squared Error: 0.02131249999999998, R^2 Score: 0.9291089620200744 Support Vector Regressor (MinMaxScaler - Top 10 Features) - Mean Squared Error: 0.01063026736761726, R^2 Score: 0.9646409061492308
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