Suppose that the following code has been executed in a Jupyter notebook. Match each of the following tasks with the corresponding code for that task. from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1) from sklearn import linear_model reg = linear_model.LinearRegression() [ Choose ] train the regression model on the reg.fit(X_train, y_train) training dataset reg.predict(X_test) reg.fit(X_test, y_test) generate predictions on the test reg.score(X_test, y_test) dataset reg.score(X_test, y_train) reg.score(X_train, y_train) determine the R-squared for the training dataset reg.predict(X_train, y_test) reg.predict(X_train) determine the R-squared for the test [ Choose ] dataset

Suppose that the following code has been executed in a Jupyter notebook. Match each of the following tasks with the corresponding code for that task. from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1) from sklearn import linear_model reg = linear_model.LinearRegression() [ Choose ] train the regression model on the reg.fit(X_train, y_train) training dataset reg.predict(X_test) reg.fit(X_test, y_test) generate predictions on the test reg.score(X_test, y_test) dataset reg.score(X_test, y_train) reg.score(X_train, y_train) determine the R-squared for the training dataset reg.predict(X_train, y_test) reg.predict(X_train) determine the R-squared for the test [ Choose ] dataset

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

See similar textbooks

Similar questions

Using truth table and functions please complete task 3. Schematic truth table should match). Must have 3 ins and 7 outs. Will upvote!(:
Implement two versions of the successor function for the 8-puzzle: one that generates all the successors at once by copying and editing the 8-puzzle data structure, and one that generates one new successor each time it is called and works by modifying the parent state directly (and undoing the modifications as needed). Write versions of iterative deepening depth-first search that use these functions and compare their performance.
Your task is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck. Implement and train a classification model for the Titanic dataset (the dataset can be found here: https://www.kaggle.com/c/titanic). Please ignore the test set (i.e., test.csv) and consider the given train set (i.e., train.csv) as the dataset. What you need to do: 1. Data cleansing 2. Split the dataset (i.e., train.csv) into a training set (80% samples) and a testing set (20% samples) 3. Train your model (see details below) 4. Report the overall classification accuracies on the training and testing sets 5. Report the precision, recall, and F-measure scores on the testing set 1. Required Model (100 pts): Implement and train a logistic regression as your classification model. • You have to use Sklearn deep learning library. • You may want to refer to this tutorial: https://bit.ly/37anOxi
Specify and implement an iterator method called sortedElements for IntSet. The iterator method sortedElements returns an iterator that will produce each element of the intset in ascending order. The intset cannot be mutated when the iterator is in use. Be sure to specify the iterator method before implementation. Do not forget to give the rep invariant and abstraction function for your implementation. Note: IntSet.java has been provided. You can directly add in the code the specification, the implementation of the iterator method sortedElements, and the inner iterator class. There is a segment of simple testing code for sortedElements in main() close to the end in the provided IntSet.java. The program will not compile until you have properly implemented sortedElements. find the provided code below: https://drive.google.com/open?id=1hANO8kVigGXwFLLJDJQLE5joM1BBeCCn
Before using the spark ML library to train the machine learning model; we are required to assemble the features into one vector. If you are given a spark dataframe DF with numerical columns ['featurel','feature2', 'feature3', 'feature4', 'label'] Import all necessary libraries and write a spark code snippet to assemble features Vector for ['featurel','feature2', 'feature3', 'feature4']
betterAddProduct Write a method in your Warehouse class to further optimize addProduct. As of now, it’s possible that an item is removed from a full sector to make room for a new product, even if there are other sectors which are not full. If the current sector is not full, add the product as normal. Otherwise perform a linear probing-like operation to try to find a non-full sector (if the current one is full). Keep incrementing the sector until you either find one with space, or you return to your original sector. If you get to Sector 9, wrap around to Sector 0. If you found a new sector with space, add the product into this sector. In a real-world scenario we would have to make sure to change the ID in our system and make sure it doesn’t conflict with an existing one. In this assignment that is not necessary since the output only contains the name. If you returned to the original sector, perform eviction and add the product as normal.
Consider the two following random arrays "a" and "b": (10 points) a = np.random.randn (2, 3) # a.shape = (2, 3) b = np.random.randn (2, 1) # b.shape=(2, 1) c = a + b What will be the shape of "c"?
The "dynamic array" data structure is used as the basis of Java's ArrayList library class. Describe this data structure and explain how it achieves (amortized) constant-time adding of elements, while also giving constant-time random access of elements. As part of your answer, make sure to explain what "amortized" means for the data structure.
Add the missing pieces from checkpoint B while using this code
How touse Python to implement an alternative approximate match using the Levenshtein edit distance?
You need to Cceate a responsive webpage that will display whether a SMS is spam or not.The user will only insert a SMS in a text field and he will be shown whether the text is spam or not.You need to code the spam detection software using Naive Bayes algorithm from scratch without using ANY libraries and you need to use PYTHON. Everytime i insert a SMS in the webpage the machine learning model should give more and more accurate result that the model should be the most accurate ever.
t for several problems you will modify some provided Python code to implement algorithms to solve the same instance of the knapsack problem. After implementing all of the code and solving the problem, you must provide a single table of all results similar to the following: Table 1: Example of results summary (numbers are not realistic) Algorithm Iterations Items Selected Weight Objective Local Search (Best Improvement) 3102 49 97 117 Local Search with Random Restarts (k = 100) Local Search with Random walk (p = 0.25) 9510 121 21 147 2102 87 32 184 etc. Knapsack Problem Definition Given n different items, where each item i has an assigned value (v.) and weight (w), select a combination of the items to maximize the total value without exceeding the weight limitations, W, of the knapsack. IMPORTANT!: When generating random problem instance set you must use the code provided and values: n = 150; max weight of 2500; and, use a seed value (for the random number generator) of 51132023.