RAG Datasets
Collection
2 items • Updated
text stringlengths 301 426 | embedding sequencelengths 768 768 |
|---|---|
Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
Overview An SVM is a supervised machine learning algorithm that uses non-probabilistic linear classification to classify data. The basic idea behind SVMs is to find a hyperplane that best separates the data points into different cl... | [
0.022429319098591805,
-0.06390882283449173,
-0.03769814223051071,
-0.007368538994342089,
0.002326985588297248,
0.029704570770263672,
-0.015658419579267502,
0.006397835444658995,
0.006184267345815897,
-0.003067479468882084,
0.07099248468875885,
0.022051699459552765,
0.01951787807047367,
0.0... |
Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
particularly useful when dealing with complex, high-dimensional datasets and can handle both linearly separable and non-linearly separable data through the use of kernel functions. SVMs have been successfully applied in various fie... | [
0.010789084248244762,
-0.03259045258164406,
-0.05156145244836807,
-0.0099331084638834,
-0.03579109534621239,
0.020354291424155235,
0.040977466851472855,
0.027082066982984543,
0.027053145691752434,
0.00030056480318307877,
0.1010153740644455,
0.05016913637518883,
0.006886330898851156,
0.0851... |
Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
bioinformatics, and have been shown to have high accuracy and generalization capabilities. Understanding SVMs For this lesson, we’ll be using the iris dataset provided by sklearn. Below shows the code for loading the data set, its ... | [
0.010061315260827541,
-0.04239724576473236,
-0.05478648841381073,
0.012172789312899113,
0.015891555696725845,
0.026092341169714928,
0.06608596444129944,
0.015299093909561634,
-0.003079161047935486,
0.0455150380730629,
0.07906000316143036,
0.016712099313735962,
0.05236683040857315,
0.091196... |
Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
attributes it describes. import pandas as pd from sklearn.datasets import load_iris iris = load_iris() print(dir(iris)) print(iris.DESCR) The first thing we want to do is get all this data into a pandas dataframe to better understa... | [
0.019218940287828445,
-0.008331888355314732,
-0.007643087301403284,
0.033461663872003555,
0.03758341073989868,
0.07548656314611435,
0.04474997892975807,
0.015278801321983337,
0.024513598531484604,
0.00463864766061306,
0.026413600891828537,
0.02151120826601982,
0.07362856715917587,
0.067749... |
Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
dataframe, add a column called flower name that applies a lambda function naming each row based on the value it has under the target column, and prints the first, second, & third datasets within the dataframe. We can see that the f... | [
0.04492231085896492,
-0.007619177456945181,
-0.03775358200073242,
0.06735417991876602,
-0.028125949203968048,
0.05973997712135315,
0.034616850316524506,
0.026848727837204933,
-0.007236530538648367,
0.008813945576548576,
0.03634605556726456,
0.01909482292830944,
0.03110736422240734,
0.05310... |
Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
versicolor flower, and the last 50 rows are for the virginica flower. df = pd.DataFrame(data=iris.data, columns=iris.feature_names) df['target'] = iris.target df['flower_name'] =df.target.apply(lambda x: iris.target_names[x]) print... | [
0.06763635575771332,
-0.009364016354084015,
-0.035326335579156876,
0.03172256052494049,
-0.020801911130547523,
0.03373158723115921,
0.00744756730273366,
0.014619714580476284,
-0.007859335280954838,
-0.00178819103166461,
0.07985078543424606,
0.0154173718765378,
0.04511125385761261,
0.063634... |
Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
Let’s graph our data to visualize it. I’m going to separate the data into 3 different dataframes, corresponding to each unique flower. import matplotlib.pyplot as plt setosa_df = df[:50] versicolor_df = df[50:100] virginica_df = df... | [
0.01624256744980812,
-0.01244007982313633,
-0.03462015837430954,
0.0807684138417244,
-0.04168831184506416,
0.0083030229434371,
0.02438943274319172,
0.014950299635529518,
0.019617347046732903,
0.015804776921868324,
0.05731429159641266,
0.0004763201577588916,
-0.0004382523475214839,
0.054110... |
Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
plt.scatter(setosa_df['sepal length (cm)'], setosa_df['sepal width (cm)'],color="green",marker='+') plt.scatter(versicolor_df['sepal length (cm)'], versicolor_df['sepal width (cm)'],color="blue",marker='.') You could imagine it’s p... | [
-0.00799842644482851,
-0.0779946893453598,
-0.03153810277581215,
-0.0006736741634085774,
-0.057779498398303986,
-0.01960243657231331,
0.02322189137339592,
0.009873216040432453,
0.01273154653608799,
0.041632384061813354,
0.06871823221445084,
0.04080330207943916,
0.024561230093240738,
0.0814... |
Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
left to the top right, separating the setosa and versicolor flowers. This imaginary line (our classification boundary) is our support vector machine. There are many ways we could draw the line though, so how we do know what’s the o... | [
0.009243190288543701,
-0.07786612212657928,
-0.03150767460465431,
0.022613758221268654,
-0.043760884553194046,
-0.030242882668972015,
0.01729031279683113,
-0.01208200491964817,
0.022999076172709465,
0.028860263526439667,
0.10362911969423294,
0.004590296186506748,
0.028791137039661407,
0.01... |
Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
between the line and the corresponding data points, is the solution. In the graph above, we see each data point has a margin (drawn in red) between it and the classification boundary. We want a line that maximizes this distance. Th... | [
-0.002378813922405243,
-0.04039820656180382,
-0.03935018554329872,
0.05481526628136635,
-0.04264090210199356,
-0.01677296869456768,
0.017089243978261948,
-0.0048956857062876225,
0.05367530882358551,
-0.01758657954633236,
0.0802346020936966,
0.04549022763967514,
0.018183888867497444,
0.0292... |
Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
hence the name support vector machine. In the case we have only 2 features like the graph above, the boundary is a line. But in the case of 3 features, the boundary is a plane. Now imagine if there are more than 3 features… while i... | [
0.02112065814435482,
-0.09762776643037796,
-0.02819034829735756,
0.01990940421819687,
-0.023000016808509827,
0.0009962082840502262,
0.08704912662506104,
-0.05059242993593216,
0.023567410185933113,
0.02384939044713974,
0.0832657590508461,
-0.02146151289343834,
0.03509657084941864,
0.1076625... |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 28