Scikit-learn is an open-source Python library that provides a wide array of useful tools for data analysis and modeling, including regression. Unlike some other modeling libraries, like statsmodels, scikit-learn does not have built-in functionality to easily generate a detailed summary of the linear regression model. However, you can still extract the most important information by using various scikit-learn methods and attributes. Here’s a detailed guide on how you can get a regression model summary from scikit-learn.

**Step 1: Import Necessary Libraries**

```
import numpy as np
import pandas as pd
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
```

**Step 2: Generate or Load Your Dataset**

You’ll need data to perform regression on. For the purposes of this article, we’ll generate a dataset for a regression problem.

```
# Generate a dataset with 100 samples and 3 features
X, y = make_regression(n_samples=100, n_features=3, noise=0.1)
# Convert arrays to pandas DataFrame and Series for better visualization
X = pd.DataFrame(X, columns=['Feature1', 'Feature2', 'Feature3'])
y = pd.Series(y, name='Target')
```

**Step 3: Split Your Data into Training and Testing Sets**

It’s a common practice to split your data into a training set and a test set. The training set is used to train the model, while the test set is used to evaluate the model’s performance on unseen data.

`X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)`

**Step 4: Fit Your Regression Model**

You can now fit your regression model using the training data.

```
# Create a LinearRegression instance
model = LinearRegression()
# Fit the model
model.fit(X_train, y_train)
```

**Step 5: Predicting and Evaluating the Model**

Once the model has been fitted, you can use it to make predictions. After making predictions, you can evaluate the performance of the model.

```
# Make predictions
y_pred = model.predict(X_test)
# Calculate the Mean Squared Error (MSE)
mse = mean_squared_error(y_test, y_pred)
# Calculate the R-squared score
r2 = r2_score(y_test, y_pred)
print(f"Mean Squared Error: {mse}")
print(f"R^2 Score: {r2}")
```

**Step 6: Extracting Regression Coefficients and Intercept**

To understand the fitted regression model, you can extract the coefficients (parameters/weights) for each feature and the intercept (bias) of the model.

```
# Coefficients
coef = pd.Series(model.coef_, index=X.columns)
print("Coefficients:")
print(coef)
# Intercept
print(f"\nIntercept: {model.intercept_}")
```

The coefficients tell you the amount of change you can expect in the target variable for a one-unit change in a feature, assuming all other features remain the same. The intercept is the expected value of the target when all features equal zero.

It’s important to note that this process provides a summary of the model in terms of its parameters and performance metrics like MSE and R². However, a full regression summary like in statsmodels would include more statistics like standard errors, p-values, and confidence intervals for the coefficients, which are not directly provided by scikit-learn. This is because scikit-learn focuses more on predictive modeling and machine learning, where such statistics are used less frequently compared to traditional statistical analysis.

If you need these additional statistics, you might want to consider using statsmodels alongside scikit-learn, or performing some additional calculations manually. You can also use bootstrapping or permutation methods to estimate confidence intervals and the significance of your model’s coefficients.