Supervised vs Unsupervised Learning in AI & ML

The main purpose of semi-supervised learning is to label parts of unlabeled data after clustering them under unsupervised learning, facilitating easier classification.

Reinforcement learning uses feedback in the form of rewards or punishments to guide models in making better decisions over time.

Studying correlation helps in exploratory data analysis and can identify relationships between variables, aiding in decision-making.

<p>Positive correlation occurs when the values of two variables move in the same direction; as one increases, the other also increases.</p> Signup and view all the answers

<p>Semi-supervised learning distinguishes itself by first clustering unlabeled data and then generating labels for easier classification.</p> Signup and view all the answers

<p>A real-world application of reinforcement learning is in training self-driving cars to make decisions based on environmental feedback.</p> Signup and view all the answers

<p>Neutral correlation is characterized by no relationship in the change of the two variables; their movements do not affect each other.</p> Signup and view all the answers

<p>Reinforcement learning resembles training a dog by using rewards for desired behaviors, encouraging the learning of specific actions.</p> Signup and view all the answers

<p>A negative correlation occurs when the values of variables X and Y change in opposite directions.</p> Signup and view all the answers

<p>The most common type of correlation coefficient used is Pearson's correlation coefficient.</p> Signup and view all the answers

<p>The range of values for correlation coefficients is from -1.0 to 1.0.</p> Signup and view all the answers

<p>Descriptive statistics analyze and summarize data from a sample or population, while inferential statistics draw conclusions about a population based on sample data.</p> Signup and view all the answers

<p>Visualization methods used in descriptive statistics include bar plots, pie charts, scatter plots, and histograms.</p> Signup and view all the answers

<p>Visualizing data with scatterplots is important as it provides a quick way to discover the type of correlation between variables.</p> Signup and view all the answers

<p>The main goal of statistics is to collect, analyze, interpret, and present data to understand information and draw conclusions.</p> Signup and view all the answers

<p>Numerical data is defined as data that consists of numbers or integers.</p> Signup and view all the answers

<p>Discrete variables have finite values, while continuous variables can have infinitely varying values.</p> Signup and view all the answers

<p>Ordinal variables are those that can be ranked or ordered. An example is student grades such as A, B, or C.</p> Signup and view all the answers

<p>The mean is calculated by summing all the data points and dividing by the count of those points. It represents the average value of the data set.</p> Signup and view all the answers

<p>Standard deviation measures how much the data points deviate from the mean, providing insight into the spread of the data.</p> Signup and view all the answers

<p>The IQR represents the range of the middle 50% of the data points, measuring the spread between the first quartile (Q1) and the third quartile (Q3).</p> Signup and view all the answers

<p>The values used were Q1 at 30 minutes and Q3 at 62 minutes, leading to an IQR of 32 minutes.</p> Signup and view all the answers

<p>Outliers are values significantly different from others in a data set and can skew results, potentially leading to misleading interpretations.</p> Signup and view all the answers

<p>Nominal variables are categories without a specific order, while ordinal variables can be ranked or ordered.</p> Signup and view all the answers

<p>Outliers are identified as any data points outside the range of $Q1 - 1.5 * IQR$ and $Q3 + 1.5 * IQR$.</p> Signup and view all the answers

<p>Understanding the IQR helps inform decisions about workplace flexibility, office location, and employee well-being.</p> Signup and view all the answers

<p>Commute times above 110 minutes are considered potential outliers.</p> Signup and view all the answers

<p>MAD describes the average absolute distance of each data point from the mean of the dataset.</p> Signup and view all the answers

<p>The mean of the waiting times is calculated as $\frac{5 + 8 + 12 + 3 + 6 + 10 + 7 + 9 + 4 + 11}{10} = 7.5$ minutes.</p> Signup and view all the answers

<p>A large IQR might suggest that the company should offer flexible work arrangements or remote work options.</p> Signup and view all the answers

<p>Identifying outliers can guide companies in making adjustments to locations, services, or employee support initiatives.</p> Signup and view all the answers

<p>The company might explore initiatives like providing transportation assistance or promoting carpooling.</p> Signup and view all the answers

<p>It ensures representation from all relevant customer segments.</p> Signup and view all the answers

<p>Age and service plan.</p> Signup and view all the answers

<p>It predicts when equipment is likely to fail, enabling proactive maintenance.</p> Signup and view all the answers

<p>They are used to recommend products or content based on user behavior.</p> Signup and view all the answers

<p>It is more complex and requires knowledge of population characteristics.</p> Signup and view all the answers

<p>It can assess the creditworthiness of loan applicants more effectively.</p> Signup and view all the answers

<p>It helps to group customers based on behavior and preferences for targeted strategies.</p> Signup and view all the answers

<p>It is more complex to implement than simple random sampling.</p> Signup and view all the answers

<p>The MAD is 2.5 minutes, indicating that, on average, customer waiting times deviate from the mean waiting time of 7.5 minutes by this amount.</p> Signup and view all the answers

<p>Variance is calculated by averaging the squared deviations from the mean, and a variance of 8.89 minutes² represents the average squared deviation of waiting times from the mean.</p> Signup and view all the answers

<p>Variance is more sensitive to outliers because it squares the deviations, amplifying the impact of larger deviations.</p> Signup and view all the answers

<p>The restaurant may prefer MAD because it is expressed in the same units as waiting times, making it easier for customers to understand.</p> Signup and view all the answers

<p>High MAD or variance suggests inconsistent waiting times, indicating potential issues that may need to be addressed in service processes.</p> Signup and view all the answers

<p>The restaurant can track changes in MAD and variance to assess the effectiveness of interventions aimed at reducing waiting times.</p> Signup and view all the answers

<p>The calculation of squared deviations allows for capturing the magnitude of deviations more effectively, especially for larger differences.</p> Signup and view all the answers

<p>A low variance value indicates that the waiting times are clustered close to the mean, suggesting more consistent service.</p> Signup and view all the answers

A learning technique that combines unsupervised and supervised learning to classify data.

A type of machine learning where a model learns to make decisions by receiving feedback in the form of rewards.

A relationship between two variables where they tend to increase or decrease together.

A statistical measure that assesses the relationship between two variables.

No relationship between variables when their movements don't affect each other.

A type of machine learning model that finds patterns in data without predefined labels.

The initial stage of data analysis where patterns and trends are looked for in the data.

Correlation enables understanding if there's a connection between variables like democracy and growth, or usage of cars and pollution.

Variables change in opposite directions.

A number that measures how strongly two variables are related.

A common correlation coefficient used in data science.

Graph used to show the relationship between two variables.

Summarizing and describing data using charts and plots.

Using sample data to draw conclusions about a larger population.

Data that consists of numbers.

The science of collecting, analyzing, interpreting, and presenting data.

A variable whose values are in a finite range (countable).

A variable that can take on any value within a range.

The average of a dataset.

The middle value in a sorted dataset.

The difference between the 75th and 25th percentiles.

Values that divide the data set into four equal parts.

A data point significantly different from other data points in a data set.

A measure of how spread out the data is around the mean.

The Interquartile Range (IQR) can be used to identify outliers in a dataset. Outliers are data points that fall significantly outside the typical range. A common rule is: any data point outside Q1 - 1.5 * IQR and Q3 + 1.5 * IQR is considered a potential outlier.

The lower bound in outlier detection is calculated as Q1 - 1.5 * IQR. Any data point below this bound is considered a potential outlier.

The upper bound in outlier detection is calculated as Q3 + 1.5 * IQR. Any data point above this bound is considered a potential outlier.

The difference between each data point and the mean, regardless of sign.

The average of all absolute deviations. It indicates the typical deviation from the mean.

The Mean Absolute Deviation (MAD) measures the average variation of the data points from the mean. It tells us how spread out the data is from the center point (mean).

The average of the squared deviations from the mean. It measures how spread out the data is.

To calculate MAD, first find the mean of the dataset. Then, calculate the absolute difference between each data point and the mean. Finally, average these absolute differences.

Understanding the IQR of business data like commute times or sales figures can be helpful in making strategic decisions.

MAD uses the absolute value of deviations, while variance squares the deviations. MAD is easier to interpret in original units but variance is more sensitive to outliers.

The IQR can inform decisions about: workplace flexibility, office location, and employee well-being. By understanding the variation in data, companies can adapt and improve.

A high MAD indicates inconsistent data, while a low MAD means data is clustered around the mean.

A high variance indicates data is widely spread out from the mean, whereas a low variance suggests data is clustered around the mean.

MAD and variance help reveal inconsistencies in service processes, enabling businesses to set realistic customer expectations and track improvements.

MAD and Variance reveal inconsistencies in performance, allowing for targeted improvements and setting realistic service expectations.

A sampling technique where the population is divided into groups based on characteristics (like age or location), and a random sample is taken from each group.

The groups or subgroups that the population is divided into in stratified random sampling.

Using machine learning to identify unusual patterns in data that could indicate a problem or irregularity.

Using machine learning to predict when equipment is likely to fail, allowing for proactive maintenance and reducing downtime.

Systems that use machine learning to suggest products, services, or content based on user preferences and behavior.

Using machine learning to assess the likelihood of a borrower defaulting on a loan.

Using machine learning to categorize customers into groups based on their behavior and preferences.

Using machine learning to show ads to specific groups of customers based on their behavior and interests