k-NN Algorithm 20 points - Due Saturday, March 11, 1159 pm Overview .pdf

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k-NN Algorithm 20 points - Due Saturday, March 11, 11:59 pm Overview k-NN is a supervised machine learning algorithm that can be used for classification. The algorithm perfoms classification based on similarity using a distance measure. In this programming assignment, you are required to implement the k-NN algorithm from scratch, using the following distance measures: - Euclidean distance: Given two vectors x=, and y=, the Euclidean distance, d(x,y ), is defined as d(x,y)=i=1N(yixi)2 - Manhattan Distance: Given two vectors x=, and y=, the Manhattan distance is defined as d(x,y)=i=1Nyixi.

k-NN Algorithm 20 points - Due Saturday, March 11, 11:59 pm Overview k-NN is a supervised
machine learning algorithm that can be used for classification. The algorithm perfoms
classification based on similarity using a distance measure. In this programming assignment, you
are required to implement the k-NN algorithm from scratch, using the following distance
measures: - Euclidean distance: Given two vectors x=, and y=, the Euclidean distance, d(x,y ), is
defined as d(x,y)=i=1N(yixi)2 - Manhattan Distance: Given two vectors x=, and y=, the
Manhattan distance is defined as d(x,y)=i=1Nyixi

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La Conchita The photos on the following pages show two mass movement events that occurred in La Conchita, California in 1995 and 2005. 1995 What type of mass movement occurred in 1995? What is your evidence for this? How does the mass wasting in the bottom right section (as you face the slope) differ from the main movement, and what is your evidence for this? 2005 What type[s] of mass movement occurred in 2005 ? What is your evidence for this? How did the 2005 event differ from the 1995 event? In the main photo from 2005 a barrier designed to protect homes is visible. What effect did this barrier have on the mass movement event? Open Google Earth Pro and enter "La Conchita, CA" in the search box. Save an image of the slide to annotate. You may annotate by printing the image and drawing on it, or by using a graphics package on your computer. Examine the La Chonchita area in Google Earth. You can zoom in, draw profiles and use any other tools to assist your investigation. You can also use information from the Gurrola et al., 2010 paper. - Outline the extent of the mass movements in 1995 and 2005 - Label the scarps at the top of the slide - Label the toe of the slide - Shade and label evidence for other mass wasting events in this area Spring 1995 J mass wasting events. Highlight their similarities and differences. In a second paragraph, describe and explain the present and future risks at these two locations..

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la a study of the influetice of environment factors on small group interaction, four grouns of five people each diccussed the use of masic in films. The people in the groups were either seated close together of far apath, and the group discussions took place either in a brightly or dimly lit room. Each participant ia the study was asked to rate the intimacy level of the discussion, wide higher numbers representing greater intimacy. Detennine if the enviroemental factors affected perceived intimacy. If wo, make all pairwise comparisons asing Tukcy's HSD scat. Howe many dependent variables are there? What is(are) it(thcy)? (V, point) How many itdkpendent variables are there? What is(arc) int(they)? How many levels are there for each IV? (Vi pint) Hypothens Teaing saep Ii (ha poiet) Strp 2: (lo poun) Slep 3: =0.05 Sep 4 (hi peiti) newect 144 if Step 5: (4 points, 2.5. for hand calculation and 1.5 for SFsS oeprut) Inclode your calculasions ea a separate sheet of paper. Also cut and puse the SPSS Oarput table below, Again, be suce to circle the F-ratio ned p-yalue. If you obtain a significate Fantio, be sute to inclode your HSD calcelatioo and takle of meah differences. Sip t. Circle your dection belew ( (Is peint) A. Hhat H. . Fait oo rosctile Step? Concluidin was the perextved intinacy the ame for each of the sou cminumenat. eriancoif (hithime Sicp 6: Circle your decision below; (12/ poinet) A. Rejet H. B. Fail to roject H. Step 7. Conelusion; Was the perceived intimacy the same for cach of the four cmnomonal ocedition? (h poing) A. Yes, the perceived iotimicy was the aume for the different nmironmertal condibens. 8. No, the perectved intimacy was not the same for the different envirungental condtions. Coodusion cootimsed. Which of the following environncatal conditions wict sighificantly ditferent with regand to the pereeived intimacy? (Citcle all than apply (1/2 poine) 1. Fir Bripe and Far Dim B. Fat Bright asd Clowe Brigh E. Fir Bockht and Clowe Din D. Far Dim wod Close Bright II. Fat Dam and Cloae Dim F. Clone Hripht and Clase Dist G. Noro of the bbene..

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Paris 2024 Olympic Geographies - an activity

Paris 2024 Olympic Geographies - an activity

microwave assisted reaction. General introduction

microwave assisted reaction. General introduction

microwave assisted reaction. General introduction

Accessible design: Minimum effort, maximum impact

Accessible design: Minimum effort, maximum impact

Accessible design: Minimum effort, maximum impact

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The basics of sentences session 2pptx copy.pptx

The basics of sentences session 2pptx copy.pptx

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How to Give a Domain for a Field in Odoo 17

How to Give a Domain for a Field in Odoo 17

SECOND SEMESTER TOPIC COVERAGE SY 2023-2024 Trends, Networks, and Critical Th...

SECOND SEMESTER TOPIC COVERAGE SY 2023-2024 Trends, Networks, and Critical Th...

SECOND SEMESTER TOPIC COVERAGE SY 2023-2024 Trends, Networks, and Critical Th...

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Web & Social Media Analytics Previous Year Question Paper.pdf

Web & Social Media Analytics Previous Year Question Paper.pdf

k-NN Algorithm 20 points - Due Saturday, March 11, 1159 pm Overview .pdf

1. k-NN Algorithm 20 points - Due Saturday, March 11, 11:59 pm Overview k-NN is a supervised machine learning algorithm that can be used for classification. The algorithm perfoms classification based on similarity using a distance measure. In this programming assignment, you are required to implement the k-NN algorithm from scratch, using the following distance measures: - Euclidean distance: Given two vectors x=, and y=, the Euclidean distance, d(x,y ), is defined as d(x,y)=i=1N(yixi)2 - Manhattan Distance: Given two vectors x=, and y=, the Manhattan distance is defined as d(x,y)=i=1Nyixi