[2024年06月15日]DSA-C02認定ガイド問題と解答トレーニング
DSA-C02認定お試しセット最新DSA-C02のPDF問題集
質問 # 29
Select the correct mappings:
I. W Weights or Coefficients of independent variables in the Linear regression model --> Model Pa-rameter II. K in the K-Nearest Neighbour algorithm --> Model Hyperparameter III. Learning rate for training a neural network --> Model Hyperparameter IV. Batch Size --> Model Parameter
- A. II,III,IV
- B. III,IV
- C. I,II
- D. I,II,III
正解：D
解説：
Explanation
Hyperparameters in Machine learning are those parameters that are explicitly defined by the user to control the learning process. These hyperparameters are used to improve the learning of the model, and their values are set before starting the learning process of the model.
What are hyperparameters?
In Machine Learning/Deep Learning, a model is represented by its parameters. In contrast, a training process involves selecting the best/optimal hyperparameters that are used by learning algorithms to provide the best result. So, what are these hyperparameters? The answer is, "Hyperparameters are defined as the parameters that are explicitly defined by the user to control the learning process." Here the prefix "hyper" suggests that the parameters are top-level parameters that are used in con-trolling the learning process. The value of the Hyperparameter is selected and set by the machine learning engineer before the learning algorithm begins training the model. Hence, these are external to the model, and their values cannot be changed during the training process.
Some examples of Hyperparameters in Machine Learning
The k in kNN or K-Nearest Neighbour algorithm
Learning rate for training a neural network
Train-test split ratio
Batch Size
Number of Epochs
Branches in Decision Tree
Number of clusters in Clustering Algorithm
Model Parameters:
Model parameters are configuration variables that are internal to the model, and a model learns them on its own. For example, W Weights or Coefficients of independentvariables in the Linear regression model. or Weights or Coefficients of independent variables in SVM, weight, and biases of a neural network, cluster centroid in clustering. Some key points for model parameters are as follows:
They are used by the model for making predictions.
They are learned by the model from the data itself
These are usually not set manually.
These are the part of the model and key to a machine learning Algorithm.
Model Hyperparameters:
Hyperparameters are those parameters that are explicitly defined by the user to control the learning process.
Some key points for model parameters are as follows:
These are usually defined manually by the machine learning engineer.
One cannot know the exact best value for hyperparameters for the given problem. The best value can be determined either by the rule of thumb or by trial and error.
Some examples of Hyperparameters are the learning rate for training a neural network, K in the KNN algorithm.
質問 # 30
Which of the following is a Python-based web application framework for visualizing data and analyzing results in a more efficient and flexible way?
- A. StreamBI
- B. Streamsets
- C. Streamlit
- D. Rapter
正解：C
解説：
Explanation
Streamlit is a Python-based web application framework for visualizing data and analyzing results in a more efficient and flexible way. It is an open source library that assists data scientists and academics to develop Machine Learning (ML) visualization dashboards in a short period of time. We can build and deploy powerful data applications with just a few lines of code.
Why Streamlit?
Currently, real-world applications are in high demand and developers are developing new libraries and frameworks to make on-the-go dashboards easier to build and deploy. Streamlit is a library that reduces your dashboard development time from days to hours. Following are some reasons to choose the Streamlit:
It is a free and open-source library.
Installing Streamlit is as simple as installing any other python package It is easy to learn because you won't need any web development experience, only a basic under-standing of Python is enough to build a data application.
It is compatible with almost all machine learning frameworks, including Tensorflow and Pytorch, Scikit-learn, and visualization libraries such as Seaborn, Altair, Plotly, and many others.
質問 # 31
Performance metrics are a part of every machine learning pipeline, Which ones are not the performance metrics used in the Machine learning?
- A. AUM
- B. Root Mean Squared Error (RMSE)
- C. AU-ROC
- D. R2 (R-Squared)
正解：A
解説：
Explanation
Every machine learning task can be broken down to either Regression or Classification, just like the performance metrics.
Metrics are used to monitor and measure the performance of a model (during training and testing), and do not need to be differentiable.
Regression metrics
Regression models have continuous output. So, we need a metric based on calculating some sort of distance between predicted and ground truth.
In order to evaluate Regression models, we'll discuss these metrics in detail:
Mean Absolute Error (MAE),
Mean Squared Error (MSE),
Root Mean Squared Error (RMSE),
R2 (R-Squared).
Mean Squared Error (MSE)
Mean squared error is perhaps the most popular metric used for regression problems. It essentially finds the average of the squared difference between the target value and the value predicted by the regression model.
Few key points related to MSE:
It's differentiable, so it can be optimized better.
It penalizes even small errors by squaring them, which essentially leads to an overestimation of how bad the model is.
Error interpretation has to be done with squaring factor(scale) in mind. For example in our Boston Housing regression problem, we got MSE=21.89 which primarily corresponds to (Prices)2.
Due to the squaring factor, it's fundamentally more prone to outliers than other metrics.
Mean Absolute Error (MAE)
Mean Absolute Error is the average of the difference between the ground truth and the predicted values.
Few key points for MAE
It's more robust towards outliers than MAE, since it doesn't exaggerate errors.
It gives us a measure of how far the predictions were from the actual output. However, since MAE uses absolute value of the residual, it doesn't give us an idea of the direction of the error, i.e. whether we're under-predicting or over-predicting the data.
Error interpretation needs no second thoughts, as it perfectly aligns with the original degree of the variable.
MAE is non-differentiable as opposed to MSE, which is differentiable.
Root Mean Squared Error (RMSE)
Root Mean Squared Error corresponds to the square root of the average of the squared difference between the target value and the value predicted by the regression model.
Few key points related to RMSE:
It retains the differentiable property of MSE.
It handles the penalization of smaller errors done by MSE by square rooting it.
Error interpretation can be done smoothly, since the scale is now the same as the random variable.
Since scale factors are essentially normalized, it's less prone to struggle in the case of outliers.
R2 Coefficient of determination
R2 Coefficient of determination actually works as a post metric, meaning it's a metric that's calcu-lated using other metrics.
The point of even calculating this coefficient is to answer the question "How much (what %) of the total variation in Y(target) is explained by the variation in X(regression line)" Few intuitions related to R2 results:
If the sum of Squared Error of the regression line is small => R2 will be close to 1 (Ideal), meaning the regression was able to capture 100% of the variance in the target variable.
Conversely, if the sum of squared error of the regression line is high=> R2 will be close to 0, meaning the regression wasn't able to capture any variance in the target variable.
You might think that the range of R2 is (0,1) but it's actually (-,1)because the ratio of squared errors of the regression line and mean can surpass the value 1 if the squared error of regression line is too high (>squared error of the mean).
Classification metrics
Classification problems are one of the world's most widely researched areas. Use cases are present in almost all production and industrial environments. Speech recognition, face recognition, textclassification - the list is endless.
Classification models have discrete output, so we need a metric that compares discrete classes in some form.
Classification Metrics evaluate a model's performance and tell you how good or bad the classification is, but each of them evaluates it in a different way.
So in order to evaluate Classification models, we'll discuss these metrics in detail:
Accuracy
Confusion Matrix (not a metric but fundamental to others)
Precision and Recall
F1-score
AU-ROC
Accuracy
Classification accuracy is perhaps the simplest metric to use and implement and is defined as the number of correct predictions divided by the total number of predictions, multiplied by 100.
We can implement this by comparing ground truth and predicted values in a loop or simply utilizing the scikit-learn module to do the heavy lifting for us (not so heavy in this case).
Confusion Matrix
Confusion Matrix is a tabular visualization of the ground-truth labels versus model predictions. Each row of the confusion matrix represents the instances in a predicted class and each column represents the instances in an actual class. Confusion Matrix is not exactly a performance metric but sort of a basis on which other metrics evaluate the results.
Each cell in the confusion matrix represents an evaluation factor. Let's understand these factors one by one:
True Positive(TP) signifies how many positive class samples your model predicted correctly.
True Negative(TN) signifies how many negative class samples your model predicted correctly.
False Positive(FP) signifies how many negative class samples your model predicted incorrectly. This factor represents Type-I error in statistical nomenclature. This error positioning in the confusion matrix depends on the choice of the null hypothesis.
False Negative(FN) signifies how many positive class samples your model predicted incorrectly. This factor represents Type-II error in statistical nomenclature. This error positioning in the confu-sion matrix also depends on the choice of the null hypothesis.
Precision
Precision is the ratio of true positives and total positives predicted
Recall/Sensitivity/Hit-Rate
A Recall is essentially the ratio of true positives to all the positives in ground truth.
Precision-Recall tradeoff
To improve your model, you can either improve precision or recall - but not both! If you try to re-duce cases of non-cancerous patients being labeled as cancerous (FN/type-II), no direct effect will take place on cancerous patients being labeled as non-cancerous.
F1-score
The F1-score metric uses a combination of precision and recall. In fact, the F1 score is the harmonic mean of the two.
AUROC (Area under Receiver operating characteristics curve)
Better known as AUC-ROC score/curves. It makes use of true positive rates(TPR) and false posi-tive rates(FPR).
質問 # 32
Select the Data Science Tools which are known to provide native connectivity to Snowflake?
- A. DvSUM
- B. DiYotta
- C. Denodo
- D. HEX
正解：D
解説：
Explanation
Hex - collaborative data science and analytics platform
Denodo - data virtualization and federation platform
DvSum - data catalog and data intelligence platform
Diyotta - data integration and migration
質問 # 33
Which type of Python UDFs let you define Python functions that receive batches of input rows as Pandas DataFrames and return batches of results as Pandas arrays or Series?
- A. Vectorized Python UDFs
- B. Scaler Python UDFs
- C. Hybrid Python UDFs
- D. MPP Python UDFs
正解：A
解説：
Explanation
Vectorized Python UDFs let you define Python functions that receive batches of input rows as Pandas DataFrames and return batches of results as Pandas arrays or Series. You call vectorized Py-thon UDFs the same way you call other Python UDFs.
Advantages of using vectorized Python UDFs compared to the default row-by-row processing pat-tern include:
The potential for better performance if your Python code operates efficiently on batches of rows.
Less transformation logic required if you are calling into libraries that operate on Pandas Data-Frames or Pandas arrays.
When you use vectorized Python UDFs:
You do not need to change how you write queries using Python UDFs. All batching is handled by the UDF framework rather than your own code.
As with non-vectorized UDFs, there is no guarantee of which instances of your handler code will see which batches of input.
質問 # 34
Which ones are the key actions in the data collection phase of Machine learning included?
- A. Label
- B. Ingest and Aggregate
- C. Probability
- D. Measure
正解：A、B
解説：
Explanation
The key actions in the data collection phase include:
Label: Labeled data is the raw data that was processed by adding one or more meaningful tags so that a model can learn from it. It will take some work to label it if such information is missing (manually or automatically).
Ingest and Aggregate: Incorporating and combining data from many data sources is part of data collection in AI.
Data collection
Collecting data for training the ML model is the basic step in the machine learning pipeline. The predictions made by ML systems can only be as good as the data on which they have been trained. Following are some of the problems that can arise in data collection:
Inaccurate data. The collected data could be unrelated to the problem statement.
Missing data. Sub-data could be missing. That could take the form of empty values in columns or missing images for some class of prediction.
Data imbalance. Some classes or categories in the data may have a disproportionately high or low number of corresponding samples. As a result, they risk being under-represented in the model.
Data bias. Depending on how the data, subjects and labels themselves are chosen, the model could propagate inherent biases on gender, politics, age or region, for example. Data bias is difficult to detect and remove.
Several techniques can be applied to address those problems:
Pre-cleaned, freely available datasets. If the problem statement (for example, image classification, object recognition) aligns with a clean, pre-existing, properly formulated dataset, then take ad-vantage of existing, open-source expertise.
Web crawling and scraping. Automated tools, bots and headless browsers can crawl and scrape websites for data.
Private data. ML engineers can create their own data. This is helpful when the amount of data required to train the model is small and the problem statement is too specific to generalize over an open-source dataset.
Custom data. Agencies can create or crowdsource the data for a fee.
質問 # 35
Which of the following Snowflake parameter can be used to Automatically Suspend Tasks which are running Data science pipelines after specified Failed Runs?
- A. SUSPEND_TASK_AUTO_NUM_FAILURES
- B. There is none as such available.
- C. SUSPEND_TASK_AFTER_NUM_FAILURES
- D. SUSPEND_TASK
正解：C
解説：
Explanation
Automatically Suspend Tasks After Failed Runs
Optionally suspend tasks automatically after a specified number of consecutive runs that either fail or time out.
This feature can reduce costs by suspending tasks that consume Snowflake credits but fail to run to completion. Failed task runs include runs in which the SQL code in the task body either produces a user error or times out. Task runs that are skipped, canceled, or that fail due to a sys-tem error are considered indeterminate and are not included in the count of failed task runs.
Set the SUSPEND_TASK_AFTER_NUM_FAILURES = num parameter on a standalone task or the root task in a DAG. When the parameter is set to a value greater than 0, the following behavior applies to runs of the standalone task or DAG:
Standalone tasks are automatically suspended after the specified number of consecutive task runs either fail or time out.
The root task is automatically suspended after the run of any single task in a DAG fails or times out the specified number of times in consecutive runs.
The parameter can be set when creating a task (using CREATE TASK) or later (using ALTER TASK). The setting applies to tasks that rely on either Snowflake-managed compute resources (i.e. serverless compute model) or user-managed compute resources (i.e. a virtual warehouse).
The SUSPEND_TASK_AFTER_NUM_FAILURES parameter can also be set at the account, database, or schema level. The setting applies to all standalone or root tasks contained in the modified object. Note that explicitly setting the parameter at a lower (i.e. more granular) level overrides the parameter value set at a higher level.
質問 # 36
Which one is not the feature engineering techniques used in ML data science world?
- A. Binning
- B. Imputation
- C. One hot encoding
- D. Statistical
正解：D
解説：
Explanation
Feature engineering is the pre-processing step of machine learning, which is used to transform raw data into features that can be used for creating a predictive model using Machine learning or statistical Modelling.
What is a feature?
Generally, all machine learning algorithms take input data to generate the output. The input data re-mains in a tabular form consisting of rows (instances or observations) and columns (variable or at-tributes), and these attributes are often known as features. For example, an image is an instance in computer vision, but a line in the image could be the feature. Similarly, in NLP, a document can be an observation, and the word count could be the feature. So, we can say a feature is an attribute that impacts a problem or is useful for the problem.
What is Feature Engineering?
Feature engineering is the pre-processing step of machine learning, which extracts features from raw data. It helps to represent an underlying problem to predictive models in a better way, which as a result, improve the accuracy of the model for unseen data. The predictive model contains predictor variables and an outcome variable, and while the feature engineering process selects the most useful predictor variables for the model.
Some of the popular feature engineering techniques include:
1. Imputation
Feature engineering deals with inappropriate data, missing values,human interruption, general errors, insufficient data sources, etc. Missing values within the dataset highly affect the performance of the algorithm, and to deal with them "Imputation" technique is used. Imputation is responsible for handling irregularities within the dataset.
For example, removing the missing values from the complete row or complete column by a huge percentage of missing values. But at the same time, to maintain the data size, it is required to impute the missing data, which can be done as:
For numerical data imputation, a default value can be imputed in a column, and missing values can be filled with means or medians of the columns.
For categorical data imputation, missing values can be interchanged with the maximum occurred value in a column.
2. Handling Outliers
Outliers are the deviated values or data points that are observed too away from other data points in such a way that they badly affect the performance of the model. Outliers can be handled with this feature engineering technique. This technique first identifies the outliers and then remove them out.
Standard deviation can be used to identify the outliers. For example, each value within a space has a definite to an average distance, but if a value is greater distant than acertain value, it can be considered as an outlier.
Z-score can also be used to detect outliers.
3. Log transform
Logarithm transformation or log transform is one of the commonly used mathematical techniques in machine learning. Log transform helps in handling the skewed data, and it makes the distribution more approximate to normal after transformation. It also reduces the effects of outliers on the data, as because of the normalization of magnitude differences, a model becomes much robust.
4. Binning
In machine learning, overfitting is one of the main issues that degrade the performance of the model and which occurs due to a greater number of parameters and noisydata. However, one of the popular techniques of feature engineering, "binning", can be used to normalize the noisy data. This process involves segmenting different features into bins.
5. Feature Split
As the name suggests, feature split is the process of splitting features intimately into two or more parts and performing to make new features. This technique helps the algorithms to better understand and learn the patterns in the dataset.
The feature splitting process enables the new features to be clustered and binned, which results in extracting useful information and improving the performance of the data models.
6. One hot encoding
One hot encoding is the popular encoding technique in machine learning. It is a technique that converts the categorical data in a form so that they can be easily understood by machine learning algorithms and hence can make a good prediction. It enables group theof categorical data without losing any information.
質問 # 37
Which one of the following is not the key component while designing External functions within Snowflake?
- A. UDF Service
- B. API Integration
- C. Proxy Service
- D. Remote Service
正解：A
解説：
Explanation
What is an External Function?
An external function calls code that is executed outside Snowflake.
The remotely executed code is known as a remote service.
Information sent to a remote service is usually relayed through a proxy service.
Snowflake stores security-related external function information in an API integration.
External Function:
An external function is a type of UDF. Unlike other UDFs, an external function does not contain its own code; instead, the external function calls code that is stored and executed outside Snowflake.
Inside Snowflake, the external function is stored as a database object that contains information that Snowflake uses to call the remote service. This stored information includes the URL of the proxy service that relays information to and from the remote service.
Remote Service:
The remotely executed code is known as a remote service.
The remote service must act like a function. For example, it must return a value.
Snowflake supports scalar external functions; the remote service must return exactly one row for each row received.
Proxy Service:
Snowflake does not call a remote service directly. Instead, Snowflake calls a proxy service, which relays the data to the remote service.
The proxy service can increase security by authenticating requests to the remote service.
The proxy service can support subscription-based billing for a remote service. For example, the proxy service can verify that a caller to the remote service is a paid subscriber.
The proxy service also relays the response from the remote service back to Snowflake.
Examples of proxy services include:
Amazon API Gateway.
Microsoft Azure API Management service.
API Integration:
An integration is a Snowflake object that provides an interface between Snowflake and third-party services.
An API integration stores information, such as security information, that is needed to work with a proxy service or remote service.
An API integration is created with the CREATE API INTEGRATION command.
Users can write and call their own remote services, or call remote services written by third parties. These remote services can be written using any HTTP server stack,including cloud serverless compute services such as AWS Lambda.
質問 # 38
Which method is used for detecting data outliers in Machine learning?
- A. BOXI
- B. Z-Score
- C. Scaler
- D. CMIYC
正解：B
解説：
Explanation
What are outliers?
Outliers are the values that look different from the other values in the data. Below is a plot high-lighting the outliers in 'red' and outliers can be seen in both the extremes of data.
Reasons for outliers in data
Errors during data entry or a faulty measuring device (a faulty sensor may result in extreme readings).
Natural occurrence (salaries of junior level employees vs C-level employees) Problems caused by outliers Outliers in the data may causes problems during model fitting (esp. linear models).
Outliers may inflate the error metrics which give higher weights to large errors (example, mean squared error, RMSE).
Z-score method is of the method for detecting outliers. This methodis generally used when a variable' distribution looks close to Gaussian. Z-score is the number of standard deviations a value of a variable is away from the variable' mean.
Z-Score = (X-mean) / Standard deviation
IQR method , Box plots are some more example of methods used to detect data outliers in Data science.
質問 # 39
Data Scientist used streams in ELT (extract, load, transform) processes where new data inserted in-to a staging table is tracked by a stream. A set of SQL statements transform and insert the stream contents into a set of production tables. Raw data is coming in the JSON format, but for analysis he needs to transform it into relational columns in the production tables. which of the following Data transformation SQL function he can used to achieve the same?
- A. He could not apply Transformation on Stream table data.
- B. lateral flatten()
- C. Transpose()
- D. METADATA$ACTION ()
正解：B
解説：
Explanation
To know about lateral flatten SQL Function, please refer:
https://docs.snowflake.com/en/sql-reference/constructs/join-lateral#example-of-using-lateral-with-flatten
質問 # 40
The most widely used metrics and tools to assess a classification model are:
- A. All of the above
- B. Area under the ROC curve
- C. Cost-sensitive accuracy
- D. Confusion matrix
正解：A
質問 # 41
Which of the following Functions do Support Windowing?
- A. EXTRACT
- B. LISTAGG
- C. HASH_AGG
- D. ENCRYPT
正解：B
解説：
Explanation
What is a Window?
A window is a group of related rows. For example, a window might be defined based on timestamps, with all rows in the same month grouped in the same window. Or a window might be defined based on location, with all rows from a particular city grouped in the same window.
A window can consist of zero, one, or multiple rows. For simplicity, Snowflake documentation usually says that a window contains multiple rows.
What is a Window Function?
A window function is any function that operates over a window of rows.
A window function is generally passed two parameters:
A row. More precisely, a window function is passed 0 or more expressions. In almost all cases, at least one of those expressions references a column in that row. (Most window functions require at least one column or expression, but a few window functions, such as some rank-related functions, do not required an explicit column or expression.) A window of related rows that includes that row. The window can be the entire table, or a subset of the rows in the table.
For non-window functions, all arguments are usually passed explicitly to the function, for example:
MY_FUNCTION(argument1, argument2, ...)
Window functions behave differently; although the current row is passed as an argument the normal way, the window is passed through a separate clause, called an OVER clause. The syntax of the OVER clause is documented later.
LISTAGG
Returns the concatenated input values, separated by the delimiter string.
Window function
1.LISTAGG( [ DISTINCT ] <expr1> [, <delimiter> ] )
2.[ WITHIN GROUP ( <orderby_clause> ) ]
3.OVER ( [ PARTITION BY <expr2> ] )
HASH_AGG
Returns an aggregate signed 64-bit hash value over the (unordered) set of input rows. HASH_AGG never returns NULL, even if no input is provided. Empty input "hashes" to 0.
Window function
HASH_AGG( [ DISTINCT ] <expr> [ , <expr2> ... ] ) OVER ( [ PARTITION BY <expr3> ] ) HASH_AGG(*) OVER ( [ PARTITION BY <expr3> ] )
質問 # 42
You are training a binary classification model to support admission approval decisions for a college degree program.
How can you evaluate if the model is fair, and doesn't discriminate based on ethnicity?
- A. Compare disparity between selection rates and performance metrics across ethnicities.
- B. None of the above.
- C. Evaluate each trained model with a validation datasetand use the model with the highest accuracy score.
- D. Remove the ethnicity feature from the training dataset.
正解：A
解説：
Explanation
By using ethnicity as a sensitive field, and comparing disparity between selection rates and performance metrics for each ethnicity value, you can evaluate the fairness of the model.
質問 # 43
Mark the correct steps for saving the contents of a DataFrame to aSnowflake table as part of Moving Data from Spark to Snowflake?
- A. Step 1.Use the PUT() method of the DataFrame to construct a DataFrameWriter.
Step 2.Specify SNOWFLAKE_SOURCE_NAME using the format() method.
Step 3.Specify the connector options using either the option() or options() method.
Step 4.Use the dbtable option to specify the table to which data is written.
Step 5.Use the save() method to specify the save mode for the content. - B. Step 1.Use the PUT() method of the DataFrame to construct a DataFrameWriter.
Step 2.Specify SNOWFLAKE_SOURCE_NAME using the NAME() method.
Step 3.Use the dbtable option to specify the table to which data is written.
Step 4.Specify the connector options using either the option() or options() method.
Step 5.Use the save() method to specify the save mode for the content. - C. Step 1.Use the write() method of the DataFrame to construct a DataFrameWriter.
Step 2.Specify SNOWFLAKE_SOURCE_NAME using the format() method.
Step 3.Specify the connector options using either the option() or options() method.
Step 4.Use the dbtable option to specify the table to which data is written.
Step 5.Use the mode() method to specify the save mode for the content.
(Correct) - D. Step 1.Use the writer() method of the DataFrame to construct a DataFrameWriter.
Step 2.Specify SNOWFLAKE_SOURCE_NAME using the format() method.
Step 3.Use the dbtable option to specify the table to which data is written.
Step 4.Specify the connector options using either the option() or options() method.
Step 5.Use the save() method to specify the save mode for the content.
正解：C
解説：
Explanation
Moving Data from Spark to Snowflake
The steps for saving the contents of a DataFrame to a Snowflake table are similar to writing from Snowflake to Spark:
1. Use the write() method of the DataFrame to construct a DataFrameWriter.
2. Specify SNOWFLAKE_SOURCE_NAME using the format() method.
3. Specify the connector options using either the option() or options() method.
4. Use the dbtable option to specify the table to which data is written.
5. Use the mode() method to specify the save mode for the content.
Examples
1.df.write
2..format(SNOWFLAKE_SOURCE_NAME)
3..options(sfOptions)
4..option("dbtable", "t2")
5..mode(SaveMode.Overwrite)
6..save()
質問 # 44
Which of the following is a useful tool for gaining insights into the relationship between features and predictions?
- A. sklearn plots
- B. numpy plots
- C. FULL dependence plots (FDP)
- D. Partial dependence plots(PDP)
正解：D
解説：
Explanation
Partial dependence plots (PDP) is a useful tool for gaining insights into the relationship between features and predictions. It helps us understand how different values of a particular feature impact model's predictions.
質問 # 45
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