Snowflake DSA-C02 問題集 練習問題・模擬試験｜日本語版＆英語版対応

Explanation: Supervised learning is a type of machine learning where we train a model using labeled data. In this learning paradigm, the model learns from the training data to make predictions or infer mappings. Conditional probability often plays a role in this, especially in algorithms like Naive Bayes, where the goal is to compute the probability of a certain class given the features (variables) of the data, which is fundamentally a conditional probability. Here's a brief rundown of the other options: A. Reinforcement learning: This is about agents who take actions in an environment to maximize cumulative reward. It's not centered around conditional probability of variables. B. Unsupervised learning: This is about finding patterns in data without labeled responses. Methods such as clustering and dimensionality reduction fall into this category. C. Artificial learning: This is not a standard term in the field of machine learning or data science.

Explanation: What is linear regression? Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable's value is called the independent variable. Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model. A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0). For linear regression Y=a+bx+error. If neglect error then Y=a+bx. If x increases by 1, then Y = a+b(x+1) which implies Y=a+bx+b. So Y increases by its slope. For linear regression Y=a+bx+error. If neglect error then Y=a+bx. If x increases by 1, then Y = a+b(x+1) which implies Y=a+bx+b. So Y increases by its slope.

Explanation: The Supervised Machine Learning algorithm can be broadly classified into Regression and Classification Algorithms. In Regression algorithms, we have predicted the output for continuous values, but to predict the categorical values, we need Classification algorithms. What is the Classification Algorithm? The Classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. In Classification, a program learns from the given dataset or observations and then classifies new observation into a number of classes or groups. Such as, Yes or No, 0 or 1, Spam or Not Spam, cat or dog, etc. Classes can be called as targets/labels or categories. Unlike regression, the output variable of Classification is a category, not a value, such as "Green or Blue", "fruit or animal", etc. Since the Classification algorithm is a Supervised learning technique, hence it takes labeled input data, which means it contains input with the corresponding output. In classification algorithm, a discrete output function(y) is mapped to input variable(x). y=f(x), where y = categorical output The best example of an ML classification algorithm is Email Spam Detector. The main goal of the Classification algorithm is to identify the category of a given dataset, and these algorithms are mainly used to predict the output for the categorical data. The algorithm which implements the classification on a dataset is known as a classifier. There are two types of Classifications: Binary Classifier: If the classification problem has only two possible outcomes, then it is called as Binary Classifier. Examples: YES or NO, MALE or FEMALE, SPAM or NOT SPAM, CAT or DOG, etc. Multi-class Classifier: If a classification problem has more than two outcomes, then it is called as Multi-class Classifier. Example: Classifications of types of crops, Classification of types of music. Binary classification in deep learning refers to the type of classification where we have two class labels C one normal and one abnormal. Some examples of binary classification use: ・ To detect whether email is spam or not ・ To determine whether or not a patient has a certain disease in medicine. ・ To determine whether or not quality specifications were met when it comes to QA (Quality Assurance). For example, the normal class label would be that a patient has the disease, and the abnormal class label would be that they do not, or vice-versa. As is with every other type of classification, it is only as good as the binary classification dataset that it has C or, in other words, the more training and data it has, the better it is.