Question

(a) Write python code that uses scikit-learn to load the diabetes dataset, split the data into a training dataset and a validation dataset, and train a linear regression model using the training dataset. What is the mean squared error computed using the validation dataset? (b) Scikit-learn tries to shield us from the details of how linear regression works, and in particular it hides the parameters of our prediction function from us. However, the parameters can be accessed with the following lines of code: $\beta_0$ = model.intercept_ $\beta_1$ = model.coef_ Write code that uses numpy to predict target values using the param- eters stored in $\beta_0$ and $\beta_1$. Also write code to check that your predicted target values agree with the predictions obtained using the command y_pred = model.predict(X_val)

          (a) Write python code that uses scikit-learn to load the diabetes dataset,
split the data into a training dataset and a validation dataset, and
train a linear regression model using the training dataset.
What is the mean squared error computed using the validation dataset?
(b) Scikit-learn tries to shield us from the details of how linear regression
works, and in particular it hides the parameters of our prediction
function from us. However, the parameters can be accessed with the
following lines of code:
$\beta_0$ = model.intercept_
$\beta_1$ = model.coef_
Write code that uses numpy to predict target values using the param-
eters stored in $\beta_0$ and $\beta_1$. Also write code to check that your
predicted target values agree with the predictions obtained using the
command
y_pred = model.predict(X_val)

Show more…

(a) Write python code that uses scikit-learn to load the diabetes dataset,
split the data into a training dataset and a validation dataset, and
train a linear regression model using the training dataset.
What is the mean squared error computed using the validation dataset?
(b) Scikit-learn tries to shield us from the details of how linear regression
works, and in particular it hides the parameters of our prediction
function from us. However, the parameters can be accessed with the
following lines of code:
β0 = model.intercept
β1 = model.coef
Write code that uses numpy to predict target values using the param-
eters stored in β0 and β1. Also write code to check that your
predicted target values agree with the predictions obtained using the
command
ypred = model.predict(Xval)

Added by Kevin M.

Computer Science and Information Technology

Trishna Knowledge Systems 2018 Edition

Instant Answer

Solved by Expert Krishna G

03/22/2022

Step 1

```python from sklearn.datasets import load_diabetes from sklearn.model_selection import train_test_split diabetes = load_diabetes() X = diabetes.data y = diabetes.target X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, Show more…

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(a) Write Python code that uses scikit-learn to load the diabetes dataset, split the data into a training dataset and a validation dataset, and train a linear regression model using the training dataset. What is the mean squared error computed using the validation dataset? (b) Scikit-learn tries to shield us from the details of how linear regression works, and in particular, it hides the parameters of our prediction function from us. However, the parameters can be accessed with the following lines of code: beta0 = model.intercept_ beta1 = model.coef_ Write code that uses NumPy to predict target values using the parameters stored in beta0 and beta1. Also, write code to check that your predicted target values agree with the predictions obtained using the command: y_pred = model.predict(X_val)

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Transcript

00:01 In this problem we are given a data of p -i -m -e indian data set of diabetes patients.

00:07 So this data can be fetched various sites such as kaggle, githab, ekstra.

00:13 So this data contained 768 rows and nine columns.

00:23 Okay, so in this question we are asked to find the accuracy of this data with, 70 % of training and 30 % testing.

00:35 Accuracy of data set with 70 % of training and 30 % testing.

00:56 Okay.

00:57 So we have few accuracy from different models.

01:01 So we will see one by one.

01:05 So first is knn.

01:08 So knn means k nearest neighbor.

01:13 Okay.

01:14 Now kn here, knn is equals to k neighbors classifier.

01:26 N neighbors are 11.

01:29 K &n fit is k train and predict predicted.

01:41 Kn is equal to knn.

01:44 Predict x test c m k n is equals to matrix confusion and accuracy kn matrix accuracy score y test predicted okay now k is equals to three so its accuracy is 0 .7385341091 and k is equals to 5 so accuracy is 0 .75 -22935 -7998 and if accuracy is and if k is equals to 11 so accuracy will be 0 .775 -264 -2 22018.

03:00 K is equals to 13.

03:02 Accuracy is 0 .76146 -7889408.

03:11 Thus, best fit at k is equals to 11.

03:20 Okay...

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