Add some information about the album below, the author, or any other background context. Make it a few sentences long so folks can pick up some informative tidbits. Then, link them off to some social networking sites or contact information.
Sponsor FileZilla development and reach developers, webmasters and bloggers
+ Welcome to the homepage of FileZilla®, the free FTP solution. The FileZilla Client not only supports FTP,
+ but also FTP over TLS (FTPS) and SFTP. It is open source software distributed free of charge under the terms of the GNU General Public License.
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Using various kinds of features together
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In this data set, we have two different features petal-length on X axis and sepal-length on Y axis.
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Using various kinds of features together allows for more nuance.
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So visualizing petal-length and sepal-length in a scatterplot helps us distinguish three different species of iris flowers.
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+ The dataset suggests that, among the three species, the Iris-setosa has the smallest petal-length and sepal-length,
+ the Iris-versicolor has the medium petal-length and sepal-length,
+ and the Iris-virginica has the largest petal-length and sepal-length.
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Drawing boundaries
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You can visualize your petal-length and sepal-length observations as the boundaries of regions in your scatterplot.
+ Dots plotted in the three regions would be in the three species of iris flowers, respectively.
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Identifying boundaries in data using math is the essence of statistical learning.
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The boundaries are called decision boundaries.
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Visualizing decision boundaries in two dimensions and three dimensions could give us a better understanding of the given data set.
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News
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Welcome to the homepage of Aliro Ed.
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This card explains some intuition using scatterplot.
Add some information about the album below, the author, or any other background context. Make it a few sentences long so folks can pick up some informative tidbits. Then, link them off to some social networking sites or contact information.
Consider the situation where you want to decide if a dot belongs in
+ Class_1 (Iris-setosa) or Class_2 (Iris-versicolor), or Class_3 (Iris-virginica).
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+ Assigning a class to a data point is called classification task in machine learning terms.
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In the left chart, the dots are the data points. The color of the dots represents the class of the data point.
+ In this example, our task is to classify the data points into one of the three classes.
+ Now, let's click the red button to project the data points on the Y axis.
+ In the current chart, dots are projected on the sepal-length axis, which is Y axis.
+ Can we find points on the Y axis that separate the dots into three classes?
+ What about the X axis? Can we find points on the X axis that separate the dots into three classes?
+ Each of the sepal-length and petal-length can be used to classify the data points into the three classes.
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+ Can we do better classify the data points into the three classes using only both of the two features? Let's move to the next page.
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Using various kinds of features together
+
In this data set, we have two different features petal-length on X axis and sepal-length on Y axis.
+
+
Using various kinds of features together allows for more nuance.
+
So visualizing petal-length and sepal-length in a scatterplot helps us distinguish three different species of iris flowers.
+
+
+ The dataset suggests that, among the three species, the Iris-setosa has the smallest petal-length and sepal-length,
+ the Iris-versicolor has the medium petal-length and sepal-length,
+ and the Iris-virginica has the largest petal-length and sepal-length.
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Drawing boundaries
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You can visualize your petal-length and sepal-length observations as the boundaries of regions in your scatterplot.
+ Dots plotted in the three regions would be in the three species of iris flowers, respectively.
+
Identifying boundaries in data using math is the essence of statistical learning.
+
The boundaries are called decision boundaries.
+
Visualizing decision boundaries in two dimensions and three dimensions could give us a better understanding of the given data set.
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This card explains some intuition using scatterplot.
Add some information about the album below, the author, or any other background context. Make it a few sentences long so folks can pick up some informative tidbits. Then, link them off to some social networking sites or contact information.
Add some information about the album below, the author, or any other background context. Make it a few sentences long so folks can pick up some informative tidbits. Then, link them off to some social networking sites or contact information.
Let's transform our visualiztion into a histogram to see how frequently each species appears at each sepal-length. Please click the green button.
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Class_1 (Iris-setosa) has the lowest average of sepal-length, Class_2 (Iris-versicolor) has the second lowest average of sepal-length, and Class_3 (Iris-virginica) has the highest average of sepal-length.
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Please click the Y axis. It will show an example to put boundaries
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Please click the Y axis. It will show an example to put boundaries to distinguish classes of iris flowers. Then please click the Different boundaries button. It will show an example to put different boundaries to distinguish classes of iris flowers.
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Adding nuance
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Adding another dimension allows for more nuance. For example, New York apartments can be extremely expensive per square foot.
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So visualizing elevation and price per square foot in a scatterplot helps us distinguish lower-elevation homes.
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The data suggests that, among homes at or below 73 meters, those that cost more than $19,116.7 per square meter are in New York City.
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Dimensions in a data set are called features, predictors, or variables. 1
You can visualize your elevation (>73 m) and price per square foot (>$19,116.7) observations as the boundaries of regions in your scatterplot. Homes plotted in the green and blue regions would be in San Francisco and New York, respectively.
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Identifying boundaries in data using math is the essence of statistical learning.
+
Of course, you’ll need additional information to distinguish homes with lower elevations and lower per-square-foot prices.
Add some information about the album below, the author, or any other background context. Make it a few sentences long so folks can pick up some informative tidbits. Then, link them off to some social networking sites or contact information.
+ In this example, we are going to use the iris data set to build a decision tree.
+ The iris data set contains 100 samples of three different species of iris flowers.
+ There are four features in the dataset: sepal length, sepal width, petal length, and petal width.
+ And we are going to use decision tree to distinguish three different species of iris flowers with four features.
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+ If petal-length is less than 2.45, 33% of data point is setosa.
+ 67% of data point has petal-length greater than 2.45.
+ Let's look at the second split.
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+ 46% of data point has petal-width greather than 1.75.
+ 93% of data point in the thrid split has petal-length greater than 4.85.
+ The number of setosa is 0.
+ The number of versicolor is 0.
+ The number of virginica is 43.
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+ 7% of data point has petal-length less than 4.85.
+ The number of setosa is 0.
+ The number of versicolor is 1.
+ The number of virginica is 2.
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+ 11% of data point has petal-length greather than 4.95.
+ The number of setosa is 0.
+ The number of versicolor is 2.
+ The number of virginica is 4.
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+ 89% of data point in the third split has petal-length less than 4.95.
+ The number of setosa is 0.
+ The number of versicolor is 47.
+ The number of virginica is 1.
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You can visualize your elevation (>73 m) and price per square foot (>$19,116.7) observations as the boundaries of regions in your scatterplot. Homes plotted in the green and blue regions would be in San Francisco and New York, respectively.
+
Identifying boundaries in data using math is the essence of statistical learning.
+
Of course, you’ll need additional information to distinguish homes with lower elevations and lower per-square-foot prices.
Add some information about the album below, the author, or any other background context. Make it a few sentences long so folks can pick up some informative tidbits. Then, link them off to some social networking sites or contact information.
Let’s say you had to determine whether a dot is in
+ Class_1 or in Class_2 or in Class_3. In machine learning terms, categorizing data points is a classification task.
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Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
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Based on the home-elevation data to the right, you could argue that a home above 73 meters should be classified as one in San Francisco.
You can visualize your elevation (>73 m) and price per square foot (>$19,116.7) observations as the boundaries of regions in your scatterplot. Homes plotted in the green and blue regions would be in San Francisco and New York, respectively.
+
Identifying boundaries in data using math is the essence of statistical learning.
+
Of course, you’ll need additional information to distinguish homes with lower elevations and lower per-square-foot prices.
Add some information about the album below, the author, or any other background context. Make it a few sentences long so folks can pick up some informative tidbits. Then, link them off to some social networking sites or contact information.
Aliro Ed is a new way to learn and teach. It is a platform that allows you to create and share interactive content with your students. Aliro is a new way to learn and teach. It is a platform that allows you to create and share interactive content with your students.
Aliro Ed is a new way to learn and teach. It is a platform that allows you to create and share interactive content with your students. Aliro is a new way to learn and teach. It is a platform that allows you to create and share interactive content with your students.
Aliro Ed is a new way to learn and teach. It is a platform that allows you to create and share interactive content with your students. Aliro is a new way to learn and teach. It is a platform that allows you to create