Conteúdo do Curso
Linear Regression with Python
Linear Regression with Python
4. Choosing The Best Model
Evaluate the Model
In this challenge, you are given the good old housing dataset, but this time only with the 'age' feature.
Let's build a scatterplot of this data.
Fitting a straight line to this data may not be a great choice. The price gets higher for either brand-new or really old houses. Fitting a parabola looks like a better choice. And that's what you will do in this challenge.
But before you start, recall the PolynomialFeatures
class.
The fit_transform(X)
method requires X
to be a 2-D array (or a DataFrame).
Using X = df[['column_name']]
will get your X
suited for fit_transform()
.
And if you have a 1-D array, use .reshape(-1, 1)
to make a 2-D array with the same contents.
The task is to build a Polynomial Regression of degree 2 using PolynomialFeatures
and OLS
.
Tarefa
- Assign the
X
variable to a DataFrame containing column'age'
. - Create an
X_tilde
matrix using thePolynomialFeatures
class. - Build and train a Polynomial Regression model.
- Print the model's parameters.
- Reshape
X_new
to be a 2-D array. - Preprocess
X_new
the same way asX
.
Tarefa
- Assign the
X
variable to a DataFrame containing column'age'
. - Create an
X_tilde
matrix using thePolynomialFeatures
class. - Build and train a Polynomial Regression model.
- Print the model's parameters.
- Reshape
X_new
to be a 2-D array. - Preprocess
X_new
the same way asX
.
Tudo estava claro?
Evaluate the Model
In this challenge, you are given the good old housing dataset, but this time only with the 'age' feature.
Let's build a scatterplot of this data.
Fitting a straight line to this data may not be a great choice. The price gets higher for either brand-new or really old houses. Fitting a parabola looks like a better choice. And that's what you will do in this challenge.
But before you start, recall the PolynomialFeatures
class.
The fit_transform(X)
method requires X
to be a 2-D array (or a DataFrame).
Using X = df[['column_name']]
will get your X
suited for fit_transform()
.
And if you have a 1-D array, use .reshape(-1, 1)
to make a 2-D array with the same contents.
The task is to build a Polynomial Regression of degree 2 using PolynomialFeatures
and OLS
.
Tarefa
- Assign the
X
variable to a DataFrame containing column'age'
. - Create an
X_tilde
matrix using thePolynomialFeatures
class. - Build and train a Polynomial Regression model.
- Print the model's parameters.
- Reshape
X_new
to be a 2-D array. - Preprocess
X_new
the same way asX
.
Tarefa
- Assign the
X
variable to a DataFrame containing column'age'
. - Create an
X_tilde
matrix using thePolynomialFeatures
class. - Build and train a Polynomial Regression model.
- Print the model's parameters.
- Reshape
X_new
to be a 2-D array. - Preprocess
X_new
the same way asX
.
Tudo estava claro?
Evaluate the Model
In this challenge, you are given the good old housing dataset, but this time only with the 'age' feature.
Let's build a scatterplot of this data.
Fitting a straight line to this data may not be a great choice. The price gets higher for either brand-new or really old houses. Fitting a parabola looks like a better choice. And that's what you will do in this challenge.
But before you start, recall the PolynomialFeatures
class.
The fit_transform(X)
method requires X
to be a 2-D array (or a DataFrame).
Using X = df[['column_name']]
will get your X
suited for fit_transform()
.
And if you have a 1-D array, use .reshape(-1, 1)
to make a 2-D array with the same contents.
The task is to build a Polynomial Regression of degree 2 using PolynomialFeatures
and OLS
.
Tarefa
- Assign the
X
variable to a DataFrame containing column'age'
. - Create an
X_tilde
matrix using thePolynomialFeatures
class. - Build and train a Polynomial Regression model.
- Print the model's parameters.
- Reshape
X_new
to be a 2-D array. - Preprocess
X_new
the same way asX
.
Tarefa
- Assign the
X
variable to a DataFrame containing column'age'
. - Create an
X_tilde
matrix using thePolynomialFeatures
class. - Build and train a Polynomial Regression model.
- Print the model's parameters.
- Reshape
X_new
to be a 2-D array. - Preprocess
X_new
the same way asX
.
Tudo estava claro?
In this challenge, you are given the good old housing dataset, but this time only with the 'age' feature.
Let's build a scatterplot of this data.
Fitting a straight line to this data may not be a great choice. The price gets higher for either brand-new or really old houses. Fitting a parabola looks like a better choice. And that's what you will do in this challenge.
But before you start, recall the PolynomialFeatures
class.
The fit_transform(X)
method requires X
to be a 2-D array (or a DataFrame).
Using X = df[['column_name']]
will get your X
suited for fit_transform()
.
And if you have a 1-D array, use .reshape(-1, 1)
to make a 2-D array with the same contents.
The task is to build a Polynomial Regression of degree 2 using PolynomialFeatures
and OLS
.
Tarefa
- Assign the
X
variable to a DataFrame containing column'age'
. - Create an
X_tilde
matrix using thePolynomialFeatures
class. - Build and train a Polynomial Regression model.
- Print the model's parameters.
- Reshape
X_new
to be a 2-D array. - Preprocess
X_new
the same way asX
.