Questions and Answers
What is the purpose of f strings?
How to use f strings?
The Video explains the concept of __________ learning using linear regression as an example.
supervised
Linear regression is a widely used machine learning algorithm that fits a __________ line to the data to predict values based on one input.
straight
Linear regression is a type of __________ model that predicts numbers as output.
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There are other types of regression models and supervised learning models, such as __________ models, which predict categories.
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The dataset used to train the model is called a __________ set, and the input is denoted by x and the output is denoted by y.
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Supervised learning algorithm inputs a dataset including both __________ features and __________ targets (right answers).
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The algorithm outputs a function () that takes a new input () and outputs an estimate (__________).
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The model makes predictions for () using a linear function of (), which is called linear regression with one variable ( ________ linear regression).
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For linear regression, the model is represented by ________.
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Which of the following is the output or 'target' variable?
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The ________ function is a crucial element in making linear regression work.
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How is y reflected in the code for linear regression?
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What are the results of the given code snippet: x_train = np.array([1.0, 2.0])?
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To measure the model's performance, a ____________ function is used, which compares the ____________(y hat) to the ____________ (y) by taking the ____________ and ____________ it, then ____________ the squared errors across the ____________ training set and dividing by ____ the number of training examples (m).
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The final cost function aims to find values for ____________ and ____________ that make the prediction y hat i close to the target y^i.
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代价函数（cost function）是用来评估算法__________与__________之间的差距的一种数学函数。
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J depends on the parameter ___ and controls the __________ of the line defined by fw.
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When does the model fit the data relatively well?
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What shape does the cost function J of w, b have?
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Study Notes
Supervised Machine Learning: Regression and Classification
- Supervised learning utilizes input (x) and output (y) data to train models using known answers.
- Linear regression is a common technique that predicts numerical outcomes by fitting a straight line to data.
- There are multiple regression models, with classification models specifically predicting categorical outcomes.
Data Fundamentals
- The training set is the dataset used to train the model, while the input features are represented as x and the output targets as y.
- The algorithm operationalizes predictions through a function denoted as f(x), which estimates outputs (y-hat) based on new inputs.
Model Functionality
- Linear regression can be expressed mathematically as fw,b(x) = wx + b, where w represents slope and b represents the intercept.
- The performance of the model is assessed using a cost function, which compares predicted values (y-hat) to actual targets (y).
Cost Functions
- The cost function quantifies the prediction error by summing the squared differences between y-hat and y across the training set, averaging this sum over the number of training examples.
- The objective of the cost function is to minimize the error by optimizing the parameters w and b, ensuring that y-hat closely approximates y.
Visualization and Analysis
- The shape of the cost function is typically represented in three dimensions resembling a soup bowl or hammock, indicating minima that reveal optimal parameter values.
- When the cost is minimized, the model effectively captures the underlying data trends, making accurate predictions.
Practical Example
- For instance, if x_train = np.array([1.0, 2.0]), the array will consist of elements reflecting those inputs, and operations can be performed to calculate the model output using the defined function.
- Implementing a cost function involves squaring the differences between predictions and actual values, aiding in the evaluation and refinement of model accuracy.
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Description
These flashcards cover key concepts from the first week of supervised machine learning, focusing on regression and classification techniques. Explore definitions and examples, including the use of f-strings in Python and the fundamentals of linear regression. Perfect for students beginning their journey into machine learning.
